diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo deleted file mode 100644 index 11e5427..0000000 --- a/docs/build/html/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file records the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 5f692cdea773b403afcc7068483ecb67 -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/.doctrees/advanced_usage.doctree b/docs/build/html/.doctrees/advanced_usage.doctree deleted file mode 100644 index d0ea96a..0000000 Binary files a/docs/build/html/.doctrees/advanced_usage.doctree and /dev/null differ diff --git a/docs/build/html/.doctrees/api.doctree b/docs/build/html/.doctrees/api.doctree deleted file mode 100644 index d6c0590..0000000 Binary files a/docs/build/html/.doctrees/api.doctree and /dev/null differ diff --git a/docs/build/html/.doctrees/configuration.doctree b/docs/build/html/.doctrees/configuration.doctree deleted file mode 100644 index 7bd42dc..0000000 Binary files a/docs/build/html/.doctrees/configuration.doctree and /dev/null differ diff --git a/docs/build/html/.doctrees/environment.pickle b/docs/build/html/.doctrees/environment.pickle deleted file mode 100644 index 33b1262..0000000 Binary files a/docs/build/html/.doctrees/environment.pickle and /dev/null differ diff --git a/docs/build/html/.doctrees/examples.doctree b/docs/build/html/.doctrees/examples.doctree deleted file mode 100644 index c74edaa..0000000 Binary files a/docs/build/html/.doctrees/examples.doctree and /dev/null differ diff --git a/docs/build/html/.doctrees/formats.doctree b/docs/build/html/.doctrees/formats.doctree deleted file mode 100644 index 54c5ad1..0000000 Binary files a/docs/build/html/.doctrees/formats.doctree and /dev/null differ diff --git a/docs/build/html/.doctrees/index.doctree b/docs/build/html/.doctrees/index.doctree deleted file mode 100644 index a9480cf..0000000 Binary files a/docs/build/html/.doctrees/index.doctree and /dev/null differ diff --git a/docs/build/html/.doctrees/installation.doctree b/docs/build/html/.doctrees/installation.doctree deleted file mode 100644 index 052e4ba..0000000 Binary files a/docs/build/html/.doctrees/installation.doctree and /dev/null differ diff --git a/docs/build/html/.doctrees/quickstart.doctree b/docs/build/html/.doctrees/quickstart.doctree deleted file mode 100644 index dd611b1..0000000 Binary files a/docs/build/html/.doctrees/quickstart.doctree and /dev/null differ diff --git a/docs/build/html/_modules/atio/core.html b/docs/build/html/_modules/atio/core.html deleted file mode 100644 index 210fbdd..0000000 --- a/docs/build/html/_modules/atio/core.html +++ /dev/null @@ -1,600 +0,0 @@ - - - - - - - - atio.core — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

atio.core의 소스 코드

-"""progress 적용 후 write 함수"""
-import os
-import tempfile
-import threading
-import time
-import numpy as np
-from queue import Queue
-from .plugins import get_writer
-from .utils import setup_logger, ProgressBar
-
-

-[문서]
-def write(obj, target_path=None, format=None, show_progress=False, verbose=False, **kwargs):
-    """
-    데이터 객체(obj)를 안전하게 target_path 또는 데이터베이스에 저장합니다.
-
-    - 파일 기반 쓰기 (format: 'csv', 'parquet', 'excel' 등):
-      - target_path (str): 필수. 데이터가 저장될 파일 경로입니다.
-      - 롤백 기능이 있는 원자적 쓰기를 수행합니다.
-
-    - 데이터베이스 기반 쓰기 (format: 'sql', 'database'):
-      - target_path: 사용되지 않습니다.
-      - kwargs (dict): 데이터베이스 쓰기에 필요한 추가 인자들입니다.
-        - pandas.to_sql: 'name'(테이블명), 'con'(커넥션 객체)가 필수입니다.
-        - polars.write_database: 'table_name', 'connection_uri'가 필수입니다.
-    
-    Args:
-        obj: 저장할 데이터 객체 (e.g., pandas.DataFrame, polars.DataFrame, np.ndarray).
-        target_path (str, optional): 파일 저장 경로. 파일 기반 쓰기 시 필수. Defaults to None.
-        format (str, optional): 저장할 포맷. Defaults to None.
-        show_progress (bool): 진행도 표시 여부. Defaults to False.
-        verbose (bool): 상세한 성능 진단 정보 출력 여부. Defaults to False.
-        **kwargs: 각 쓰기 함수에 전달될 추가 키워드 인자.
-    """
-    logger = setup_logger(debug_level=verbose)
-    t0 = time.perf_counter()
-
-    # --- 1. 데이터베이스 쓰기 특별 처리 ---
-    # 데이터베이스 쓰기는 파일 경로 기반의 원자적 쓰기 로직을 따르지 않습니다.
-    if format in ('sql', 'database'):
-        logger.info(f"데이터베이스 쓰기 모드 시작 (format: {format})")
-        writer_method_name = get_writer(obj, format)
-        
-        if writer_method_name is None:
-            err_msg = f"객체 타입 {type(obj).__name__}에 대해 지원하지 않는 format: {format}"
-            logger.error(err_msg)
-            raise ValueError(err_msg)
-
-        try:
-            writer_func = getattr(obj, writer_method_name)
-            
-            # 각 데이터베이스 쓰기 함수에 필요한 필수 인자 확인
-            if format == 'sql': # Pandas
-                if 'name' not in kwargs or 'con' not in kwargs:
-                    raise ValueError("'name'(테이블명)과 'con'(DB 커넥션) 인자는 'sql' 포맷에 필수입니다.")
-            elif format == 'database': # Polars
-                if 'table_name' not in kwargs or 'connection_uri' not in kwargs:
-                    raise ValueError("'table_name'과 'connection_uri' 인자는 'database' 포맷에 필수입니다.")
-            
-            # target_path는 무시하고 **kwargs로 받은 인자들을 사용하여 DB에 직접 씁니다.
-            writer_func(**kwargs)
-            
-            t_end = time.perf_counter()
-            logger.info(f"✅ 데이터베이스 쓰기 완료 (총 소요 시간: {t_end - t0:.4f}s)")
-            return # DB 쓰기 완료 후 함수 종료
-
-        except Exception as e:
-            t_err = time.perf_counter()
-            logger.error(f"데이터베이스 쓰기 중 예외 발생: {e}")
-            logger.info(f"데이터베이스 쓰기 실패 (소요 시간: {t_err - t0:.4f}s, 에러: {type(e).__name__})")
-            raise e
-
-    # --- 2. 파일 기반 원자적 쓰기 ---
-    if target_path is None:
-        raise ValueError("파일 기반 쓰기(예: 'csv', 'parquet')에는 'target_path' 인자가 필수입니다.")
-
-    dir_name = os.path.dirname(os.path.abspath(target_path))
-    base_name = os.path.basename(target_path)
-    os.makedirs(dir_name, exist_ok=True)
-
-    # 롤백을 위한 백업 경로 설정
-    backup_path = target_path + "._backup"
-    original_exists = os.path.exists(target_path)
-    
-    with tempfile.TemporaryDirectory(dir=dir_name) as tmpdir:
-        tmp_path = os.path.join(tmpdir, base_name)
-        logger.info(f"임시 디렉토리 생성: {tmpdir}")
-        logger.info(f"임시 파일 경로: {tmp_path}")
-        
-        t1 = time.perf_counter()
-        
-        writer = get_writer(obj, format)
-        if writer is None:
-            logger.error(f"지원하지 않는 format: {format}")
-            if verbose:
-                logger.debug(f"Atomic write step timings (FAILED at setup): "
-                            f"setup={t1-t0:.4f}s, total={time.perf_counter()-t0:.4f}s")
-            logger.info(f"Atomic write failed at setup stage (took {time.perf_counter()-t0:.4f}s)")
-            raise ValueError(f"지원하지 않는 format: {format}")
-        logger.info(f"사용할 writer: {writer} (format: {format})")
-
-        try:
-            if not show_progress:
-                _execute_write(writer, obj, tmp_path, **kwargs)
-            else:
-                _execute_write_with_progress(writer, obj, tmp_path, **kwargs)
-            
-            t2 = time.perf_counter()
-            logger.info(f"데이터 임시 파일에 저장 완료: {tmp_path}")
-
-        except Exception as e:
-            # 쓰기 실패 시에는 롤백할 필요가 없음 (원본 파일은 그대로 있음)
-            t_error = time.perf_counter()
-            logger.error(f"임시 파일 저장 중 예외 발생: {e}")
-            if verbose:
-                logger.debug(f"Atomic write step timings (ERROR during write): "
-                            f"setup={t1-t0:.4f}s, write_call={t_error-t1:.4f}s (실패), "
-                            f"total={t_error-t0:.4f}s, error_type={type(e).__name__}")
-            logger.info(f"Atomic write failed during write stage (took {t_error-t0:.4f}s, error: {type(e).__name__})")
-            raise e
-
-        # [롤백 STEP 1] 기존 파일 백업
-        if original_exists:
-            logger.info(f"기존 파일 백업: {target_path} -> {backup_path}")
-            try:
-                # rename은 atomic 연산이므로 백업 과정도 안전합니다.
-                os.rename(target_path, backup_path)
-            except Exception as e:
-                logger.error(f"백업 생성 실패. 작업을 중단합니다: {e}")
-                # 백업 실패 시 더 이상 진행하면 안 되므로 예외를 발생시킵니다.
-                raise IOError(f"Failed to create backup for {target_path}") from e
-
-        try:
-            # [롤백 STEP 2] 원자적 교체
-            os.replace(tmp_path, target_path)
-            t3 = time.perf_counter()
-            logger.info(f"원자적 교체 완료: {tmp_path} -> {target_path}")
-            
-            # [롤백 STEP 3] _SUCCESS 플래그 생성
-            success_path = os.path.join(os.path.dirname(target_path), f".{os.path.basename(target_path)}._SUCCESS")
-            with open(success_path, "w") as f:
-                f.write("OK\n")
-            t4 = time.perf_counter()
-            logger.info(f"_SUCCESS 플래그 파일 생성: {success_path}")
-            
-            # [롤백 STEP 4] 성공 시 백업 파일 삭제
-            if original_exists:
-                os.remove(backup_path)
-                logger.info(f"작업 성공, 백업 파일 삭제 완료: {backup_path}")
-
-            if verbose:
-                logger.debug(f"Atomic write step timings (SUCCESS): "
-                             f"setup={t1-t0:.4f}s, write_call={t2-t1:.4f}s, "
-                             f"replace={t3-t2:.4f}s, success_flag={t4-t3:.4f}s, "
-                             f"total={t4-t0:.4f}s")
-            logger.info(f"✅ Atomic write completed successfully (took {t4-t0:.4f}s)")
-
-        except Exception as e:
-            # [롤백 STEP 5] 교체 또는 플래그 생성 실패 시 롤백 실행
-            t_final_error = time.perf_counter()
-            logger.error(f"최종 저장 단계에서 오류 발생. 롤백을 시작합니다. 원인: {e}")
-
-            if original_exists:
-                try:
-                    # 새로 쓴 불완전한 파일이 있다면 삭제
-                    if os.path.exists(target_path):
-                        os.remove(target_path)
-                    
-                    # 백업해둔 원본 파일을 다시 복구
-                    os.rename(backup_path, target_path)
-                    logger.info(f"롤백 성공: 원본 파일 복구 완료 ({backup_path} -> {target_path})")
-                
-                except Exception as rollback_e:
-                    logger.critical(f"치명적 오류: 롤백 실패! {rollback_e}")
-                    logger.critical(f"시스템이 불안정한 상태일 수 있습니다. 수동 확인이 필요합니다.")
-                    logger.critical(f"남아있는 파일: (새 데이터) {target_path}, (원본 백업) {backup_path}")
-
-            if verbose:
-                 logger.debug(f"Atomic write step timings (FAILED AND ROLLED BACK): "
-                             f"setup={t1-t0:.4f}s, write_call={t2-t1:.4f}s, "
-                             f"final_stage_fail_time={t_final_error-t2:.4f}s, "
-                             f"total={t_final_error-t0:.4f}s, error_type={type(e).__name__}")
-            logger.info(f"Atomic write failed and rolled back (took {t_final_error-t0:.4f}s, error: {type(e).__name__})")
-            
-            # 원본 예외를 다시 발생시켜 사용자에게 알립니다.
-            raise e

-
-
-def _execute_write(writer, obj, path, **kwargs):
-    """
-    내부 쓰기 실행 함수. 핸들러 타입에 따라 분기하여 실제 쓰기 작업을 수행합니다.
-    - callable(writer): `np.save`와 같은 함수 핸들러
-    - str(writer): `to_csv`와 같은 객체의 메소드 핸들러
-    """
-    # 1. writer가 호출 가능한 '함수'인 경우 (e.g., np.save, np.savetxt)
-    if callable(writer):
-        # 1a. np.savez, np.savez_compressed 특별 처리: 여러 배열을 dict로 받아 저장
-        if writer in (np.savez, np.savez_compressed):
-            if not isinstance(obj, dict):
-                raise TypeError(
-                    f"'{writer.__name__}'로 여러 배열을 저장하려면, "
-                    f"데이터 객체는 dict 타입이어야 합니다. (현재: {type(obj).__name__})"
-                )
-            writer(path, **obj)
-        # 1b. 그 외 일반적인 함수 핸들러 처리
-        else:
-            writer(path, obj, **kwargs)
-
-    # 2. writer가 '메소드 이름(문자열)'인 경우 (e.g., 'to_csv', 'to_excel')
-    # 이 경우, obj.to_csv(path, **kwargs) 와 같이 호출됩니다.
-    else:
-        getattr(obj, writer)(path, **kwargs)
-
-def _execute_write_with_progress(writer, obj, path, **kwargs):
-    """멀티스레딩으로 쓰기 작업과 진행도 표시를 함께 실행하는 내부 함수"""
-    stop_event = threading.Event()
-    exception_queue = Queue()
-
-    # 실제 쓰기 작업을 수행할 '작업 스레드'의 목표 함수
-    def worker_task():
-        try:
-            _execute_write(writer, obj, path, **kwargs)
-        except Exception as e:
-            exception_queue.put(e)
-
-    # 스레드 생성
-    worker_thread = threading.Thread(target=worker_task)
-    progress_bar = ProgressBar(filepath=path, stop_event=stop_event, description="Writing")
-    monitor_thread = threading.Thread(target=progress_bar.run)
-
-    # 스레드 시작
-    worker_thread.start()
-    monitor_thread.start()
-
-    # 작업 스레드가 끝날 때까지 대기
-    worker_thread.join()
-
-    # 모니터 스레드에 중지 신호 전송 및 종료 대기
-    stop_event.set()
-    monitor_thread.join()
-
-    # 작업 스레드에서 예외가 발생했는지 확인하고, 있었다면 다시 발생시킴
-    if not exception_queue.empty():
-        raise exception_queue.get_nowait()
-
-import uuid
-from .utils import read_json, write_json
-
-

-[문서]
-def write_snapshot(obj, table_path, mode='overwrite', format='parquet', **kwargs):
-    logger = setup_logger(debug_level=False)
-
-    # 1. 경로 설정 및 폴더 생성
-    os.makedirs(os.path.join(table_path, 'data'), exist_ok=True)
-    os.makedirs(os.path.join(table_path, 'metadata'), exist_ok=True)
-    
-    # 2. 현재 버전 확인
-    pointer_path = os.path.join(table_path, '_current_version.json')
-    current_version = 0
-    if os.path.exists(pointer_path):
-        current_version = read_json(pointer_path)['version_id']
-    new_version = current_version + 1
-
-    # 3. 임시 디렉토리 내에서 모든 작업 수행
-    with tempfile.TemporaryDirectory() as tmpdir:
-        # 3a. 새 데이터 파일 쓰기
-        writer = get_writer(obj, format)
-        data_filename = f"{uuid.uuid4()}.{format}"
-        tmp_data_path = os.path.join(tmpdir, data_filename)
-        _execute_write(writer, obj, tmp_data_path, **kwargs)
-
-       # 3. 임시 디렉토리 내에서 모든 작업 수행
-    with tempfile.TemporaryDirectory() as tmpdir:
-        # 3a. 새 데이터 파일 쓰기
-        writer = get_writer(obj, format)
-        if writer is None:
-            raise ValueError(f"지원하지 않는 format: {format} for object type {type(obj)}")
-            
-        data_filename = f"{uuid.uuid4()}.{format}"
-        tmp_data_path = os.path.join(tmpdir, data_filename)
-        _execute_write(writer, obj, tmp_data_path, **kwargs)
-
-        # 3b. 새 manifest 생성
-        new_manifest = {
-            'files': [{'path': os.path.join('data', data_filename), 'format': format}]
-        }
-        manifest_filename = f"manifest-{uuid.uuid4()}.json"
-        write_json(new_manifest, os.path.join(tmpdir, manifest_filename))
-
-        # 3c. 새 snapshot 생성을 위한 준비
-        all_manifests = [os.path.join('metadata', manifest_filename)]
-
-        if mode.lower() == 'append' and current_version > 0:
-            try:
-                prev_metadata_path = os.path.join(table_path, 'metadata', f'v{current_version}.metadata.json')
-                prev_metadata = read_json(prev_metadata_path)
-                prev_snapshot_filename = prev_metadata['snapshot_filename']
-                
-                prev_snapshot_path = os.path.join(table_path, prev_snapshot_filename)
-                prev_snapshot = read_json(prev_snapshot_path)
-                existing_manifests = prev_snapshot['manifests']
-                
-                all_manifests.extend(existing_manifests)
-            except (FileNotFoundError, KeyError):
-                logger.warning(f"Append mode: 이전 버전(v{current_version})의 메타데이터를 찾을 수 없거나 형식이 올바르지 않습니다. Overwrite 모드로 동작합니다.")
-
-        # 3d. 최종 manifest 목록으로 새 snapshot 생성
-        snapshot_id = int(time.time())
-        snapshot_filename = f"snapshot-{snapshot_id}-{uuid.uuid4()}.json"
-        
-        new_snapshot = {
-            'snapshot_id': snapshot_id,
-            'timestamp': time.time(),
-            'manifests': all_manifests
-        }
-        write_json(new_snapshot, os.path.join(tmpdir, snapshot_filename))
-        
-        # 3e. 새 version metadata 생성
-        new_metadata = {
-            'version_id': new_version,
-            'snapshot_id': snapshot_id,
-            'snapshot_filename': os.path.join('metadata', snapshot_filename)
-        }
-        metadata_filename = f"v{new_version}.metadata.json"
-        write_json(new_metadata, os.path.join(tmpdir, metadata_filename))
-
-        # 3f. 새 포인터 파일 생성
-        new_pointer = {'version_id': new_version}
-        tmp_pointer_path = os.path.join(tmpdir, '_current_version.json')
-        write_json(new_pointer, tmp_pointer_path)
-
-        # 4. 최종 커밋
-        os.rename(tmp_data_path, os.path.join(table_path, 'data', data_filename))
-        os.rename(os.path.join(tmpdir, manifest_filename), os.path.join(table_path, 'metadata', manifest_filename))
-        os.rename(os.path.join(tmpdir, snapshot_filename), os.path.join(table_path, 'metadata', snapshot_filename))
-        os.rename(os.path.join(tmpdir, metadata_filename), os.path.join(table_path, 'metadata', metadata_filename))
-        os.replace(tmp_pointer_path, pointer_path)
-        logger.info(f"스냅샷 쓰기 완료! '{table_path}'가 버전 {new_version}으로 업데이트되었습니다.")

-
-
-
-
-

-[문서]
-def read_table(table_path, version=None, output_as='pandas'):
-    # 1. 읽을 버전 결정 및 진입점(metadata.json) 찾기
-    pointer_path = os.path.join(table_path, '_current_version.json')
-    if version is None:
-        version_id = read_json(pointer_path)['version_id']
-    else:
-        version_id = version
-    
-    metadata_path = os.path.join(table_path, 'metadata', f'v{version_id}.metadata.json')
-    metadata = read_json(metadata_path)
-    snapshot_filepath = metadata['snapshot_filename']
-
-    # 2. metadata -> snapshot -> manifest 순으로 파싱
-    snapshot_path = os.path.join(table_path, snapshot_filepath) # 정확한 경로 사용
-    snapshot = read_json(snapshot_path)
-    
-    # 3. 모든 manifest를 읽어 최종 데이터 파일 목록 취합
-    all_data_files = []
-    for manifest_ref in snapshot['manifests']:
-        manifest_path = os.path.join(table_path, manifest_ref)
-        manifest = read_json(manifest_path)
-        for file_info in manifest['files']:
-            # file_info 에는 path, format 등의 정보가 있음
-            all_data_files.append(os.path.join(table_path, file_info['path']))
-
-    # 4. output_as 옵션에 따라 최종 데이터 객체 생성
-    if not all_data_files:
-        # 데이터가 없는 경우 처리
-        return None # 또는 빈 DataFrame
-
-    if output_as == 'pandas':
-        import pandas as pd
-        return pd.read_parquet(all_data_files)
-    elif output_as == 'polars':
-        import polars as pl
-        return pl.read_parquet(all_data_files)
-    # NumPy 등의 다른 형식 처리 로직 추가
-    
-    raise ValueError(f"지원하지 않는 출력 형식: {output_as}")

-
-
-
-from datetime import datetime, timedelta
-
-

-[문서]
-def expire_snapshots(table_path, keep_for=timedelta(days=7), dry_run=True):
-    """
-    설정된 보관 기간(keep_for)보다 오래된 스냅샷과
-    더 이상 참조되지 않는 데이터 파일을 삭제합니다.
-    """
-    logger = setup_logger()
-    now = datetime.now()
-    metadata_dir = os.path.join(table_path, 'metadata')
-    
-    if not os.path.isdir(metadata_dir):
-        logger.info("정리할 테이블이 없거나 메타데이터 폴더를 찾을 수 없습니다.")
-        return
-
-    # --- 1. 모든 메타데이터 정보와 파일명 수집 ---
-    all_versions_meta = {}      # version_id -> version_meta
-    all_snapshots_meta = {}     # snapshot_id -> snapshot_meta
-    all_manifest_paths = set()  # 모든 manifest 파일 경로
-    
-    for filename in os.listdir(metadata_dir):
-        path = os.path.join(metadata_dir, filename)
-        if filename.startswith('v') and filename.endswith('.metadata.json'):
-            meta = read_json(path)
-            all_versions_meta[meta['version_id']] = meta
-        elif filename.startswith('snapshot-'):
-            snap = read_json(path)
-            all_snapshots_meta[snap['snapshot_id']] = snap
-        elif filename.startswith('manifest-'):
-            all_manifest_paths.add(os.path.join('metadata', filename))
-
-    # --- 2. "살아있는" 객체 식별 ---
-    live_snapshot_ids = set()
-    live_manifests = set()
-    live_data_files = set()
-
-    # 현재 버전을 포함하여 보관 기간 내의 모든 버전을 "살아있는" 것으로 간주
-    for version_meta in all_versions_meta.values():
-        snapshot_id = version_meta['snapshot_id']
-        snapshot = all_snapshots_meta.get(snapshot_id)
-        
-        if snapshot and (now - datetime.fromtimestamp(snapshot['timestamp'])) < keep_for:
-            live_snapshot_ids.add(snapshot_id)
-            for manifest_ref in snapshot.get('manifests', []):
-                live_manifests.add(manifest_ref)
-                manifest_path = os.path.join(table_path, manifest_ref)
-                if os.path.exists(manifest_path):
-                    manifest_data = read_json(manifest_path)
-                    for file_info in manifest_data.get('files', []):
-                        live_data_files.add(file_info['path'])
-
-    # --- 3. 삭제할 "고아" 객체 식별 ---
-    files_to_delete = []
-
-    # 고아 데이터 파일 찾기
-    data_dir = os.path.join(table_path, 'data')
-    if os.path.isdir(data_dir):
-        for data_file in os.listdir(data_dir):
-            relative_path = os.path.join('data', data_file)
-            if relative_path not in live_data_files:
-                files_to_delete.append(os.path.join(table_path, relative_path))
-
-    # 고아 매니페스트 파일 찾기
-    manifests_to_delete = all_manifest_paths - live_manifests
-    for manifest_path in manifests_to_delete:
-        files_to_delete.append(os.path.join(table_path, manifest_path))
-
-    # 고아 스냅샷 및 버전 메타데이터 파일 찾기
-    for version_id, version_meta in all_versions_meta.items():
-        snapshot_id = version_meta['snapshot_id']
-        if snapshot_id not in live_snapshot_ids:
-            # vX.metadata.json 파일 삭제 대상 추가
-            files_to_delete.append(os.path.join(metadata_dir, f"v{version_id}.metadata.json"))
-            # snapshot-X.json 파일 삭제 대상 추가
-            snapshot_filename = version_meta.get('snapshot_filename') # 이전 단계에서 이 키를 추가했었음
-            if snapshot_filename:
-                 files_to_delete.append(os.path.join(table_path, snapshot_filename))
-    
-    # 중복 제거
-    files_to_delete = sorted(list(set(files_to_delete)))
-
-    # --- 4. 최종 삭제 실행 ---
-    if not files_to_delete:
-        logger.info("삭제할 오래된 파일이 없습니다.")
-        return
-
-    logger.info(f"총 {len(files_to_delete)}개의 오래된 파일을 찾았습니다.")
-    if dry_run:
-        logger.info("[Dry Run] 아래 파일들이 삭제될 예정입니다:")
-        for f in files_to_delete:
-            print(f"  - {f}")
-    else:
-        logger.info("오래된 파일들을 삭제합니다...")
-        for f in files_to_delete:
-            try:
-                os.remove(f)
-                logger.debug(f"  - 삭제됨: {f}")
-            except OSError as e:
-                logger.error(f"  - 삭제 실패: {f}, 오류: {e}")
-        logger.info("삭제 작업이 완료되었습니다.")

-
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/_modules/atio/plugins.html b/docs/build/html/_modules/atio/plugins.html deleted file mode 100644 index c45d8a7..0000000 --- a/docs/build/html/_modules/atio/plugins.html +++ /dev/null @@ -1,229 +0,0 @@ - - - - - - - - atio.plugins — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

atio.plugins의 소스 코드

-from .utils import setup_logger
-# 추후 Pandas, Polars, Numpy 등 확장 지원을 위한 플러그인 구조
-
-logger = setup_logger()
-
-# { 객체 타입: { 포맷: 핸들러 } }
-# 핸들러는 '메소드 이름(str)' 또는 '호출 가능한 함수'가 될 수 있습니다.
-WRITER_MAPPING = {}
-
-

-[문서]
-def register_writer(obj_type, fmt, handler):
-    """(객체 타입, 포맷) 쌍으로 쓰기 핸들러를 등록"""
-    if obj_type not in WRITER_MAPPING:
-        WRITER_MAPPING[obj_type] = {}
-    WRITER_MAPPING[obj_type][fmt] = handler
-    logger.debug(f"Writer registered: type={obj_type.__name__}, format={fmt}, handler={handler}")

-
-
-

-[문서]
-def get_writer(obj, fmt):
-    """객체의 타입과 포맷에 맞는 핸들러를 조회"""
-    obj_type = type(obj)
-    handler = WRITER_MAPPING.get(obj_type, {}).get(fmt)
-    if handler is None:
-        logger.warning(f"No writer found for type {obj_type.__name__} and format '{fmt}'")
-    return handler

-
-
-# ---------------------------------------------------------------------------
-# 1. Pandas 쓰기 방법 등록
-# ---------------------------------------------------------------------------
-try:
-    import pandas as pd
-
-    PANDAS_DF_TYPE = pd.DataFrame
-    
-    # Pandas DataFrame에 대한 쓰기 핸들러 등록
-    # 값은 DataFrame 객체의 메소드 이름(문자열)입니다.
-    # 예: format='csv' -> df.to_csv(...) 호출
-    register_writer(PANDAS_DF_TYPE, "csv", "to_csv")
-    register_writer(PANDAS_DF_TYPE, "parquet", "to_parquet")
-    register_writer(PANDAS_DF_TYPE, "json", "to_json")
-    register_writer(PANDAS_DF_TYPE, "pickle", "to_pickle")
-    register_writer(PANDAS_DF_TYPE, "html", "to_html")
-    
-    # Excel 쓰기. `openpyxl` 라이브러리가 필요합니다.
-    # pip install openpyxl
-    register_writer(PANDAS_DF_TYPE, "excel", "to_excel")
-
-    # SQL 쓰기. `sqlalchemy` 라이브러리가 필요합니다.
-    # 이 핸들러는 core.py에서 특별 처리됩니다 (파일 시스템을 사용하지 않음).
-    # pip install sqlalchemy
-    register_writer(PANDAS_DF_TYPE, "sql", "to_sql")
-    
-    logger.info("Pandas writers registered successfully.")
-
-except ImportError:
-    logger.info("Pandas not found. Skipping pandas writer registration.")
-    pass
-
-# ---------------------------------------------------------------------------
-# 2. Polars 쓰기 방법 등록
-# ---------------------------------------------------------------------------
-try:
-    import polars as pl
-
-    POLARS_DF_TYPE = pl.DataFrame
-
-    # Polars DataFrame에 대한 쓰기 핸들러 등록
-    register_writer(POLARS_DF_TYPE, "csv", "write_csv")
-    register_writer(POLARS_DF_TYPE, "parquet", "write_parquet")
-    register_writer(POLARS_DF_TYPE, "json", "write_json")
-    register_writer(POLARS_DF_TYPE, "ipc", "write_ipc")
-    register_writer(POLARS_DF_TYPE, "avro", "write_avro")
-    
-    # Polars Excel 쓰기. `xlsx2csv`와 `openpyxl`이 필요할 수 있습니다.
-    # pip install xlsx2csv openpyxl
-    register_writer(POLARS_DF_TYPE, "excel", "write_excel")
-    
-    # Polars 데이터베이스 쓰기. connector-x 가 필요합니다.
-    # pip install connectorx
-    # 이 핸들러는 core.py에서 특별 처리됩니다.
-    register_writer(POLARS_DF_TYPE, "database", "write_database")
-
-    logger.info("Polars writers registered successfully.")
-
-except ImportError:
-    logger.info("Polars not found. Skipping polars writer registration.")
-    pass
-
-# ---------------------------------------------------------------------------
-# 3. NumPy 쓰기 방법 등록
-# ---------------------------------------------------------------------------
-try:
-    import numpy as np
-    
-    NUMPY_NDARRAY_TYPE = np.ndarray
-
-    # NumPy는 저장 방식이 메소드와 함수가 섞여있어 구분이 중요합니다.
-    # 값: 실제 '함수 객체' (호출 방식: np.save(path, arr))
-    register_writer(NUMPY_NDARRAY_TYPE, "npy", np.save)
-    register_writer(NUMPY_NDARRAY_TYPE, "npz", np.savez)
-    register_writer(NUMPY_NDARRAY_TYPE, "npz_compressed", np.savez_compressed)
-    register_writer(NUMPY_NDARRAY_TYPE, "csv", np.savetxt)
-    
-    # 값: '메소드 이름(문자열)' (호출 방식: arr.tofile(path))
-    register_writer(NUMPY_NDARRAY_TYPE, "bin", "tofile")
-
-    # 여러 배열을 한 번에 저장하기 위해 dict 타입도 지원
-    register_writer(dict, "npz", np.savez)
-    register_writer(dict, "npz_compressed", np.savez_compressed)
-
-    logger.info("NumPy writers registered successfully.")
-
-except ImportError:
-    logger.info("NumPy not found. Skipping numpy writer registration.")
-    pass
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/_modules/atio/utils.html b/docs/build/html/_modules/atio/utils.html deleted file mode 100644 index cc30da0..0000000 --- a/docs/build/html/_modules/atio/utils.html +++ /dev/null @@ -1,248 +0,0 @@ - - - - - - - - atio.utils — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

atio.utils의 소스 코드

-import logging
-import os
-
-
-

-[문서]
-def setup_logger(name="atio", debug_level=False):
-    logger = logging.getLogger(name)
-    if not logger.handlers:
-        handler = logging.StreamHandler()
-        formatter = logging.Formatter("[%(levelname)s] %(message)s")
-        handler.setFormatter(formatter)
-        logger.addHandler(handler)
-    
-    # debug_level이 True이면 DEBUG 레벨로 설정, 아니면 INFO 레벨
-    if debug_level:
-        logger.setLevel(logging.DEBUG)
-    else:
-        logger.setLevel(logging.INFO)
-    
-    return logger

-
-
-
-

-[문서]
-def check_file_exists(path):
-    return os.path.exists(path)

-
-
-
-import time
-import threading
-
-
-

-[문서]
-class ProgressBar:
-    """
-    파일 쓰기 진행 상황을 콘솔에 표시하는 클래스.
-    스피너, 처리된 용량, 처리 속도, 경과 시간을 표시합니다.
-    """
-
-

-[문서]
-    def __init__(self, filepath: str, stop_event: threading.Event, description: str = "Writing"):
-        self.filepath = filepath
-        self.stop_event = stop_event
-        self.description = description
-        
-        self.spinner_chars = "⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏"
-        self.start_time = time.time()

-
-        
-    def _format_size(self, size_bytes: int) -> str:
-        """바이트를 KB, MB, GB 등 읽기 좋은 형태로 변환합니다."""
-        if size_bytes < 1024:
-            return f"{size_bytes} B"
-        elif size_bytes < 1024**2:
-            return f"{size_bytes/1024:.1f} KB"
-        elif size_bytes < 1024**3:
-            return f"{size_bytes/1024**2:.1f} MB"
-        else:
-            return f"{size_bytes/1024**3:.1f} GB"
-
-

-[문서]
-    def run(self):
-        """
-        진행도 막대를 실행하는 메인 루프.
-        이 함수가 모니터링 스레드에서 실행됩니다.
-        """
-        spinner_index = 0
-        while not self.stop_event.is_set():
-            spinner_char = self.spinner_chars[spinner_index % len(self.spinner_chars)]
-            
-            try:
-                current_size = os.path.getsize(self.filepath)
-            except FileNotFoundError:
-                current_size = 0
-
-            elapsed_time = time.time() - self.start_time
-            
-            # 0으로 나누기 방지
-            speed = current_size / elapsed_time if elapsed_time > 0 else 0
-            
-            # 시간 포맷팅 (MM:SS)
-            mins, secs = divmod(int(elapsed_time), 60)
-            time_str = f"{mins:02d}:{secs:02d}"
-
-            # 최종 출력 문자열 생성
-            progress_line = (
-                f"\r{spinner_char} {self.description} {os.path.basename(self.filepath)}... "
-                f"[ {self._format_size(current_size)} | {self._format_size(speed)}/s | {time_str} ]"
-            )
-            
-            # 콘솔에 한 줄 출력 (덮어쓰기)
-            print(progress_line, end="", flush=True)
-            
-            spinner_index += 1
-            time.sleep(0.1)  # 0.1초마다 업데이트하여 CPU 사용 최소화
-        
-        # 루프가 끝나면 마지막 완료 메시지를 출력
-        self._finish()

-
-
-    def _finish(self):
-        """작업 완료 후 깔끔한 최종 메시지를 출력합니다."""
-        final_size = os.path.getsize(self.filepath)
-        elapsed_time = time.time() - self.start_time
-        time_str = f"{int(elapsed_time)}s"
-        
-        # 기존 줄을 지우기 위해 공백으로 덮어씁니다.
-        clear_line = "\r" + " " * 80 + "\r"
-        
-        finish_message = (
-            f"✔︎ Finished {self.description} {os.path.basename(self.filepath)}. "
-            f"({self._format_size(final_size)} in {time_str})"
-        )
-        print(clear_line + finish_message, flush=True)

-
-
-
-import json
-
-

-[문서]
-def read_json(path: str):
-    with open(path, 'r', encoding='utf-8') as f:
-        return json.load(f)

-
-
-

-[문서]
-def write_json(data: dict, path: str):
-    with open(path, 'w', encoding='utf-8') as f:
-        json.dump(data, f, indent=4)

-
-
- -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html deleted file mode 100644 index 4ce3508..0000000 --- a/docs/build/html/_modules/index.html +++ /dev/null @@ -1,112 +0,0 @@ - - - - - - - - 개요: 모듈 코드 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • 개요: 모듈 코드
    • -
  • -
    • -
-
-
-
-
- -

코드를 확인할 수 있는 모든 모듈

- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/_sources/advanced_usage.rst.txt b/docs/build/html/_sources/advanced_usage.rst.txt deleted file mode 100644 index f8eafc9..0000000 --- a/docs/build/html/_sources/advanced_usage.rst.txt +++ /dev/null @@ -1,323 +0,0 @@ -고급 사용법 -========== - -이 섹션에서는 Atio의 고급 기능들을 다룹니다. - -스냅샷 기반 버전 관리 --------------------- - -Atio는 데이터의 버전을 관리할 수 있는 스냅샷 시스템을 제공합니다. - -기본 스냅샷 쓰기 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - - # 데이터 생성 - df = pd.DataFrame({ - "id": [1, 2, 3], - "name": ["Alice", "Bob", "Charlie"] - }) - - # 스냅샷으로 저장 (버전 관리) - atio.write_snapshot(df, "users_table", format="parquet") - - # 새로운 데이터로 업데이트 - df_new = pd.DataFrame({ - "id": [1, 2, 3, 4], - "name": ["Alice", "Bob", "Charlie", "David"] - }) - - # append 모드로 스냅샷 추가 - atio.write_snapshot(df_new, "users_table", mode="append", format="parquet") - -스냅샷 읽기 -~~~~~~~~~~ - -.. code-block:: python - - # 최신 버전 읽기 - latest_data = atio.read_table("users_table") - - # 특정 버전 읽기 - version_1_data = atio.read_table("users_table", version=1) - - # Polars로 읽기 - polars_data = atio.read_table("users_table", output_as="polars") - -스냅샷 정리 -~~~~~~~~~~ - -.. code-block:: python - - from datetime import timedelta - - # 7일 이상 된 스냅샷 삭제 (dry run) - atio.expire_snapshots("users_table", keep_for=timedelta(days=7), dry_run=True) - - # 실제 삭제 실행 - atio.expire_snapshots("users_table", keep_for=timedelta(days=7), dry_run=False) - -데이터베이스 연동 ----------------- - -Pandas와 Polars를 사용하여 데이터베이스에 안전하게 데이터를 저장할 수 있습니다. - -Pandas SQL 연동 -~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - from sqlalchemy import create_engine - - # 데이터베이스 연결 - engine = create_engine('postgresql://user:password@localhost/dbname') - - df = pd.DataFrame({ - "id": [1, 2, 3], - "name": ["Alice", "Bob", "Charlie"] - }) - - # SQL 데이터베이스에 저장 - atio.write(df, format="sql", name="users", con=engine) - -Polars 데이터베이스 연동 -~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import polars as pl - - df = pl.DataFrame({ - "id": [1, 2, 3], - "name": ["Alice", "Bob", "Charlie"] - }) - - # 데이터베이스에 저장 - atio.write(df, format="database", - table_name="users", - connection_uri="postgresql://user:password@localhost/dbname") - -성능 최적화 ------------ - -진행도 표시 -~~~~~~~~~~ - -대용량 파일 처리 시 진행 상황을 실시간으로 확인할 수 있습니다. - -.. code-block:: python - - import atio - import pandas as pd - import numpy as np - - # 대용량 데이터 생성 - large_df = pd.DataFrame(np.random.randn(1000000, 10)) - - # 진행도 표시와 함께 저장 - atio.write(large_df, "large_data.parquet", - format="parquet", - show_progress=True) - -상세 로깅 -~~~~~~~~~ - -성능 진단을 위한 상세한 로깅을 활성화할 수 있습니다. - -.. code-block:: python - - # 상세한 성능 정보 출력 - atio.write(df, "data.parquet", format="parquet", verbose=True) - -이를 통해 다음과 같은 정보를 확인할 수 있습니다: - -- 각 단계별 소요 시간 -- 임시 파일 생성 및 교체 과정 -- 백업 및 롤백 과정 -- 성능 병목점 분석 - -에러 처리 ---------- - -Atio는 다양한 에러 상황에 대해 안전하게 처리합니다. - -파일 시스템 에러 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - - df = pd.DataFrame({"a": [1, 2, 3]}) - - try: - # 권한이 없는 디렉토리에 저장 시도 - atio.write(df, "/root/data.parquet", format="parquet") - except PermissionError as e: - print(f"권한 에러: {e}") - # 원본 파일은 그대로 보존됨 - -포맷 에러 -~~~~~~~~~ - -.. code-block:: python - - try: - # 지원하지 않는 포맷 사용 - atio.write(df, "data.unknown", format="unknown") - except ValueError as e: - print(f"지원하지 않는 포맷: {e}") - -데이터베이스 에러 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - try: - # 잘못된 데이터베이스 연결 정보 - atio.write(df, format="sql", - name="users", - con="invalid_connection") - except Exception as e: - print(f"데이터베이스 에러: {e}") - -플러그인 확장 ------------- - -Atio는 플러그인 아키텍처를 통해 새로운 형식을 쉽게 추가할 수 있습니다. - -커스텀 형식 등록 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - from atio.plugins import register_writer - import pandas as pd - - # 커스텀 형식 등록 - def custom_writer(df, path, **kwargs): - # 커스텀 저장 로직 - with open(path, 'w') as f: - f.write("Custom format\n") - f.write(df.to_string()) - - # Pandas DataFrame에 대한 커스텀 형식 등록 - register_writer(pd.DataFrame, "custom", custom_writer) - - # 사용 - df = pd.DataFrame({"a": [1, 2, 3]}) - atio.write(df, "data.custom", format="custom") - -NumPy 배열 처리 --------------- - -NumPy 배열의 다양한 저장 방식을 지원합니다. - -단일 배열 저장 -~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import numpy as np - - arr = np.array([[1, 2, 3], [4, 5, 6]]) - - # .npy 파일로 저장 - atio.write(arr, "array.npy", format="npy") - - # .csv 파일로 저장 - atio.write(arr, "array.csv", format="csv") - -여러 배열 저장 -~~~~~~~~~~~~~ - -.. code-block:: python - - # 여러 배열을 딕셔너리로 저장 - arrays = { - "features": np.random.randn(1000, 10), - "labels": np.random.randint(0, 2, 1000), - "metadata": np.array([1, 2, 3, 4, 5]) - } - - # 압축된 .npz 파일로 저장 - atio.write(arrays, "data.npz", format="npz_compressed") - -실제 사용 사례 -------------- - -머신러닝 파이프라인 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - from sklearn.model_selection import train_test_split - from sklearn.ensemble import RandomForestClassifier - - # 데이터 로드 및 전처리 - df = pd.read_csv("raw_data.csv") - - # 전처리된 데이터를 안전하게 저장 - atio.write(df, "processed_data.parquet", format="parquet") - - # 학습/테스트 분할 - X_train, X_test, y_train, y_test = train_test_split( - df.drop('target', axis=1), df['target'], test_size=0.2 - ) - - # 분할된 데이터를 스냅샷으로 저장 - atio.write_snapshot(X_train, "training_data", format="parquet") - atio.write_snapshot(X_test, "test_data", format="parquet") - - # 모델 학습 - model = RandomForestClassifier() - model.fit(X_train, y_train) - - # 예측 결과를 안전하게 저장 - predictions = model.predict(X_test) - results_df = pd.DataFrame({ - 'actual': y_test, - 'predicted': predictions - }) - - atio.write(results_df, "predictions.parquet", format="parquet") - -ETL 파이프라인 -~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - from sqlalchemy import create_engine - - # 1. 원본 데이터 로드 - raw_data = pd.read_csv("source_data.csv") - - # 2. 데이터 정제 - cleaned_data = raw_data.dropna() - cleaned_data = cleaned_data[cleaned_data['value'] > 0] - - # 3. 안전하게 정제된 데이터 저장 - atio.write(cleaned_data, "cleaned_data.parquet", format="parquet") - - # 4. 데이터베이스에 저장 - engine = create_engine('postgresql://user:password@localhost/warehouse') - atio.write(cleaned_data, format="sql", - name="processed_table", - con=engine, - if_exists='replace') - - # 5. 스냅샷으로 버전 관리 - atio.write_snapshot(cleaned_data, "daily_processed", format="parquet") \ No newline at end of file diff --git a/docs/build/html/_sources/api.rst.txt b/docs/build/html/_sources/api.rst.txt deleted file mode 100644 index 2e0f4f9..0000000 --- a/docs/build/html/_sources/api.rst.txt +++ /dev/null @@ -1,222 +0,0 @@ -API 참조 -======== - -Atio의 모든 함수와 클래스에 대한 상세한 API 문서입니다. - -주요 함수 --------- - -.. automodule:: atio - :members: - :undoc-members: - :show-inheritance: - -Core 모듈 --------- - -.. automodule:: atio.core - :members: - :undoc-members: - :show-inheritance: - -write() -------- - -안전한 원자적 파일 쓰기를 수행하는 메인 함수입니다. - -.. function:: atio.write(obj, target_path=None, format='parquet', **kwargs) - - :param obj: 저장할 데이터 객체 (pandas.DataFrame, polars.DataFrame, numpy.ndarray) - :param target_path: 저장할 파일 경로 (파일 저장 시 필수) - :param format: 저장 형식 ('csv', 'parquet', 'excel', 'json', 'sql', 'database') - :param show_progress: 진행률 표시 여부 (기본값: False) - :param verbose: 상세 성능 정보 출력 여부 (기본값: False) - :param **kwargs: 형식별 추가 매개변수 - - :returns: None - - :raises: ValueError, IOError, DatabaseError - - **사용 예제:** - - .. code-block:: python - - import atio - import pandas as pd - - df = pd.DataFrame({"a": [1, 2, 3]}) - - # 기본 사용법 - atio.write(df, "data.parquet", format="parquet") - - # 진행률 표시 - atio.write(df, "data.parquet", format="parquet", show_progress=True) - - # 성능 정보 출력 - atio.write(df, "data.parquet", format="parquet", verbose=True) - -write_snapshot() ---------------- - -데이터 스냅샷을 생성하여 버전 관리를 수행합니다. - -.. function:: atio.write_snapshot(obj, table_name, format='parquet', **kwargs) - - :param obj: 저장할 데이터 객체 - :param table_name: 테이블 이름 (스냅샷 디렉토리명) - :param format: 저장 형식 - :param **kwargs: 추가 매개변수 - - :returns: 생성된 스냅샷 ID - - **사용 예제:** - - .. code-block:: python - - # 스냅샷 생성 - snapshot_id = atio.write_snapshot(df, "users", format="parquet") - print(f"생성된 스냅샷 ID: {snapshot_id}") - -read_table() ------------ - -스냅샷에서 데이터를 읽어옵니다. - -.. function:: atio.read_table(table_name, snapshot_id='latest', format='parquet', **kwargs) - - :param table_name: 테이블 이름 - :param snapshot_id: 스냅샷 ID (기본값: 'latest') - :param format: 읽을 형식 - :param **kwargs: 추가 매개변수 - - :returns: 데이터 객체 - - **사용 예제:** - - .. code-block:: python - - # 최신 스냅샷 읽기 - df = atio.read_table("users", format="parquet") - - # 특정 스냅샷 읽기 - df = atio.read_table("users", snapshot_id="20240101_120000", format="parquet") - -expire_snapshots() ------------------ - -오래된 스냅샷을 정리합니다. - -.. function:: atio.expire_snapshots(table_name, days=30, format='parquet') - - :param table_name: 테이블 이름 - :param days: 보관할 일수 (기본값: 30) - :param format: 형식 - - :returns: 삭제된 스냅샷 수 - - **사용 예제:** - - .. code-block:: python - - # 30일 이상 된 스냅샷 정리 - deleted_count = atio.expire_snapshots("users", days=30) - print(f"삭제된 스냅샷 수: {deleted_count}") - -Plugins 모듈 ------------ - -.. automodule:: atio.plugins - :members: - :undoc-members: - :show-inheritance: - -Utils 모듈 ---------- - -.. automodule:: atio.utils - :members: - :undoc-members: - :show-inheritance: - -지원하는 형식 ------------ - -CSV 형식 -~~~~~~~ - -.. code-block:: python - - atio.write(df, "data.csv", format="csv", index=False, encoding='utf-8') - -**지원 매개변수:** -- `index`: 인덱스 포함 여부 -- `encoding`: 인코딩 방식 -- `sep`: 구분자 -- `na_rep`: NA 값 표현 - -Parquet 형식 -~~~~~~~~~~~ - -.. code-block:: python - - atio.write(df, "data.parquet", format="parquet", compression='snappy') - -**지원 매개변수:** -- `compression`: 압축 방식 ('snappy', 'gzip', 'brotli') -- `engine`: 엔진 ('pyarrow', 'fastparquet') - -Excel 형식 -~~~~~~~~~ - -.. code-block:: python - - atio.write(df, "data.xlsx", format="excel", sheet_name="Sheet1") - -**지원 매개변수:** -- `sheet_name`: 시트 이름 -- `engine`: 엔진 ('openpyxl', 'xlsxwriter') - -JSON 형식 -~~~~~~~~~ - -.. code-block:: python - - atio.write(df, "data.json", format="json", orient="records") - -**지원 매개변수:** -- `orient`: 방향 ('records', 'split', 'index', 'columns', 'values', 'table') - -SQL 형식 -~~~~~~~~ - -.. code-block:: python - - atio.write(df, format="sql", name="table_name", con=engine, if_exists="replace") - -**지원 매개변수:** -- `name`: 테이블 이름 -- `con`: 데이터베이스 연결 -- `if_exists`: 테이블 존재 시 동작 ('fail', 'replace', 'append') - -예외 처리 --------- - -Atio는 다음과 같은 예외를 발생시킬 수 있습니다: - -- **ValueError**: 잘못된 매개변수나 형식 -- **IOError**: 파일 시스템 오류 -- **DatabaseError**: 데이터베이스 연결 오류 -- **ImportError**: 필요한 패키지가 설치되지 않은 경우 - -**예외 처리 예제:** - -.. code-block:: python - - try: - atio.write(df, "data.parquet", format="parquet") - except ValueError as e: - print(f"매개변수 오류: {e}") - except IOError as e: - print(f"파일 시스템 오류: {e}") - except Exception as e: - print(f"예상치 못한 오류: {e}") \ No newline at end of file diff --git a/docs/build/html/_sources/configuration.rst.txt b/docs/build/html/_sources/configuration.rst.txt deleted file mode 100644 index 6d805cc..0000000 --- a/docs/build/html/_sources/configuration.rst.txt +++ /dev/null @@ -1,491 +0,0 @@ -설정 및 구성 -============ - -Atio의 다양한 설정 옵션과 구성 방법을 설명합니다. - -로깅 설정 --------- - -Atio는 상세한 로깅을 통해 작업 과정을 추적할 수 있습니다. - -기본 로깅 -~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - - df = pd.DataFrame({"a": [1, 2, 3]}) - - # 기본 로깅 (INFO 레벨) - atio.write(df, "data.parquet", format="parquet") - -**출력 예시:** -``` -[INFO] 임시 디렉토리 생성: /tmp/tmp12345 -[INFO] 임시 파일 경로: /tmp/tmp12345/data.parquet -[INFO] 사용할 writer: to_parquet (format: parquet) -[INFO] 데이터 임시 파일에 저장 완료: /tmp/tmp12345/data.parquet -[INFO] 원자적 교체 완료: /tmp/tmp12345/data.parquet -> data.parquet -[INFO] _SUCCESS 플래그 파일 생성: .data.parquet._SUCCESS -[INFO] ✅ Atomic write completed successfully (took 0.1234s) -``` - -상세 로깅 -~~~~~~~~~ - -.. code-block:: python - - # 상세한 성능 정보 출력 (DEBUG 레벨) - atio.write(df, "data.parquet", format="parquet", verbose=True) - -**출력 예시:** -``` -[INFO] 임시 디렉토리 생성: /tmp/tmp12345 -[INFO] 임시 파일 경로: /tmp/tmp12345/data.parquet -[INFO] 사용할 writer: to_parquet (format: parquet) -[INFO] 데이터 임시 파일에 저장 완료: /tmp/tmp12345/data.parquet -[INFO] 원자적 교체 완료: /tmp/tmp12345/data.parquet -> data.parquet -[INFO] _SUCCESS 플래그 파일 생성: .data.parquet._SUCCESS -[DEBUG] Atomic write step timings (SUCCESS): setup=0.0012s, write_call=0.0987s, replace=0.0001s, success_flag=0.0001s, total=0.1001s -[INFO] ✅ Atomic write completed successfully (took 0.1001s) -``` - -진행도 표시 ----------- - -대용량 파일 처리 시 진행 상황을 실시간으로 확인할 수 있습니다. - -기본 진행도 표시 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - import numpy as np - - # 대용량 데이터 생성 - large_df = pd.DataFrame(np.random.randn(1000000, 10)) - - # 진행도 표시와 함께 저장 - atio.write(large_df, "large_data.parquet", - format="parquet", - show_progress=True) - -**출력 예시:** -``` -⠋ Writing large_data.parquet... [ 45.2 MB | 12.3 MB/s | 00:03 ] -⠙ Writing large_data.parquet... [ 67.8 MB | 11.9 MB/s | 00:05 ] -⠹ Writing large_data.parquet... [ 89.1 MB | 12.1 MB/s | 00:07 ] -✅ Writing completed successfully (89.1 MB in 7s) -``` - -진행도 표시 옵션 -~~~~~~~~~~~~~~~ - -진행도 표시는 다음과 같은 정보를 제공합니다: - -- **스피너**: 작업 진행 상태를 시각적으로 표시 -- **파일 크기**: 현재까지 저장된 데이터 크기 -- **처리 속도**: 초당 처리되는 데이터 양 -- **경과 시간**: 작업 시작 후 경과한 시간 - -성능 최적화 설정 ----------------- - -메모리 사용량 최적화 -~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 대용량 데이터 처리 시 메모리 효율적인 설정 - atio.write(large_df, "data.parquet", format="parquet", - compression='snappy', # 빠른 압축 - index=False) # 인덱스 제외로 메모리 절약 - -압축 설정 -~~~~~~~~~ - -.. code-block:: python - - # 속도 우선 (압축 없음) - atio.write(df, "data.parquet", format="parquet", compression=None) - - # 균형 (snappy 압축) - atio.write(df, "data.parquet", format="parquet", compression='snappy') - - # 용량 우선 (gzip 압축) - atio.write(df, "data.parquet", format="parquet", compression='gzip') - -임시 디렉토리 설정 ------------------ - -기본적으로 Atio는 시스템의 임시 디렉토리를 사용합니다. - -사용자 정의 임시 디렉토리 -~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import os - import tempfile - - # 임시 디렉토리 설정 - tempfile.tempdir = "/path/to/custom/temp" - - # 또는 환경 변수 설정 - os.environ['TMPDIR'] = "/path/to/custom/temp" - -**주의사항:** -- 임시 디렉토리는 충분한 디스크 공간이 있어야 합니다 -- 쓰기 권한이 있어야 합니다 -- 빠른 I/O 성능을 위해 SSD를 권장합니다 - -에러 처리 설정 --------------- - -Atio는 다양한 에러 상황에 대해 안전하게 처리합니다. - -롤백 동작 -~~~~~~~~~ - -.. code-block:: python - - # 기본적으로 롤백이 자동으로 수행됩니다 - try: - atio.write(df, "data.parquet", format="parquet") - except Exception as e: - # 에러 발생 시 원본 파일은 보존됩니다 - print(f"저장 실패: {e}") - # 임시 파일은 자동으로 정리됩니다 - -백업 파일 관리 -~~~~~~~~~~~~~ - -.. code-block:: python - - # 백업 파일은 작업 성공 시 자동으로 삭제됩니다 - # 실패 시에는 롤백 후 삭제됩니다 - - # 백업 파일이 남아있는 경우 수동으로 확인 - import os - backup_file = "data.parquet._backup" - if os.path.exists(backup_file): - print("백업 파일이 존재합니다. 수동 확인이 필요할 수 있습니다.") - -완료 플래그 시스템 ------------------- - -Atio는 작업 완료를 확인할 수 있는 플래그 파일을 생성합니다. - -플래그 파일 확인 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - import os - - # 저장 완료 후 플래그 파일 확인 - atio.write(df, "data.parquet", format="parquet") - - # 플래그 파일 경로 - flag_file = ".data.parquet._SUCCESS" - - if os.path.exists(flag_file): - print("저장이 성공적으로 완료되었습니다.") - else: - print("저장이 완료되지 않았거나 실패했습니다.") - -플래그 파일 활용 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 배치 처리에서 완료 여부 확인 - files_to_process = ["data1.parquet", "data2.parquet", "data3.parquet"] - - for file in files_to_process: - flag_file = f".{file}._SUCCESS" - if not os.path.exists(flag_file): - print(f"{file} 처리가 완료되지 않았습니다.") - # 재처리 로직 - -스냅샷 설정 ----------- - -스냅샷 시스템의 다양한 설정 옵션을 설명합니다. - -스냅샷 모드 -~~~~~~~~~~ - -.. code-block:: python - - # overwrite 모드 (기본값) - atio.write_snapshot(df, "table_path", mode="overwrite", format="parquet") - - # append 모드 (기존 데이터에 추가) - atio.write_snapshot(df, "table_path", mode="append", format="parquet") - -스냅샷 정리 설정 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - from datetime import timedelta - - # 7일 이상 된 스냅샷 삭제 - atio.expire_snapshots("table_path", - keep_for=timedelta(days=7), - dry_run=True) # 실제 삭제 전 확인 - - # 30일 이상 된 스냅샷 삭제 - atio.expire_snapshots("table_path", - keep_for=timedelta(days=30), - dry_run=False) # 실제 삭제 - -환경 변수 설정 -------------- - -Atio의 동작을 제어하는 환경 변수들을 설정할 수 있습니다. - -로깅 레벨 설정 -~~~~~~~~~~~~~ - -.. code-block:: python - - import os - - # DEBUG 레벨로 로깅 설정 - os.environ['ATIO_LOG_LEVEL'] = 'DEBUG' - - # INFO 레벨로 로깅 설정 (기본값) - os.environ['ATIO_LOG_LEVEL'] = 'INFO' - -임시 디렉토리 설정 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 임시 디렉토리 경로 설정 - os.environ['ATIO_TEMP_DIR'] = '/path/to/temp' - - # 또는 시스템 임시 디렉토리 설정 - os.environ['TMPDIR'] = '/path/to/temp' - -성능 모니터링 설정 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 성능 모니터링 활성화 - os.environ['ATIO_PERFORMANCE_MONITORING'] = 'true' - - # 성능 모니터링 비활성화 - os.environ['ATIO_PERFORMANCE_MONITORING'] = 'false' - -플러그인 설정 ------------- - -커스텀 플러그인을 등록하여 새로운 형식을 지원할 수 있습니다. - -플러그인 등록 -~~~~~~~~~~~~ - -.. code-block:: python - - from atio.plugins import register_writer - import pandas as pd - - # 커스텀 형식 등록 - def custom_writer(df, path, **kwargs): - # 커스텀 저장 로직 - with open(path, 'w') as f: - f.write("Custom format\n") - f.write(df.to_string()) - - # 등록 - register_writer(pd.DataFrame, "custom", custom_writer) - -플러그인 확인 -~~~~~~~~~~~~ - -.. code-block:: python - - from atio.plugins import WRITER_MAPPING - - # 등록된 플러그인 확인 - for obj_type, formats in WRITER_MAPPING.items(): - print(f"Object type: {obj_type.__name__}") - for fmt, handler in formats.items(): - print(f" - {fmt}: {handler}") - -설정 파일 사용 -------------- - -설정을 파일로 관리하여 일관된 설정을 유지할 수 있습니다. - -JSON 설정 파일 -~~~~~~~~~~~~~~ - -.. code-block:: python - - import json - - # 설정 파일 생성 - config = { - "default_format": "parquet", - "compression": "snappy", - "show_progress": True, - "verbose": False, - "temp_dir": "/path/to/temp" - } - - with open("atio_config.json", "w") as f: - json.dump(config, f, indent=2) - - # 설정 파일 읽기 - with open("atio_config.json", "r") as f: - config = json.load(f) - - # 설정 적용 - atio.write(df, "data.parquet", - format=config.get("default_format", "parquet"), - compression=config.get("compression", "snappy"), - show_progress=config.get("show_progress", False), - verbose=config.get("verbose", False)) - -YAML 설정 파일 -~~~~~~~~~~~~~ - -.. code-block:: python - - import yaml - - # 설정 파일 생성 - config = { - "default_format": "parquet", - "compression": "snappy", - "show_progress": True, - "verbose": False, - "temp_dir": "/path/to/temp" - } - - with open("atio_config.yaml", "w") as f: - yaml.dump(config, f) - - # 설정 파일 읽기 - with open("atio_config.yaml", "r") as f: - config = yaml.safe_load(f) - -모범 사례 --------- - -프로덕션 환경 설정 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 프로덕션 환경을 위한 설정 - import os - import tempfile - - # 1. 전용 임시 디렉토리 설정 - tempfile.tempdir = "/var/tmp/atio" - os.makedirs(tempfile.tempdir, exist_ok=True) - - # 2. 로깅 레벨 설정 - os.environ['ATIO_LOG_LEVEL'] = 'INFO' - - # 3. 성능 최적화 설정 - def safe_write(df, path, **kwargs): - return atio.write(df, path, - format="parquet", - compression="snappy", - show_progress=True, - verbose=False, - **kwargs) - -개발 환경 설정 -~~~~~~~~~~~~~ - -.. code-block:: python - - # 개발 환경을 위한 설정 - import os - - # 1. 상세 로깅 활성화 - os.environ['ATIO_LOG_LEVEL'] = 'DEBUG' - - # 2. 성능 모니터링 활성화 - os.environ['ATIO_PERFORMANCE_MONITORING'] = 'true' - - # 3. 개발용 설정 - def dev_write(df, path, **kwargs): - return atio.write(df, path, - format="parquet", - compression=None, # 압축 없음으로 빠른 처리 - show_progress=True, - verbose=True, # 상세 정보 출력 - **kwargs) - -설정 검증 --------- - -설정이 올바르게 적용되었는지 확인하는 방법을 설명합니다. - -기본 검증 -~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - import tempfile - - # 테스트 데이터 생성 - df = pd.DataFrame({"test": [1, 2, 3]}) - - # 설정 테스트 - def test_config(): - # 임시 디렉토리 확인 - print(f"임시 디렉토리: {tempfile.gettempdir()}") - - # 로깅 테스트 - atio.write(df, "test.parquet", format="parquet", verbose=True) - - # 플래그 파일 확인 - import os - if os.path.exists(".test.parquet._SUCCESS"): - print("설정이 올바르게 작동합니다.") - else: - print("설정에 문제가 있을 수 있습니다.") - -성능 테스트 -~~~~~~~~~~ - -.. code-block:: python - - import time - import pandas as pd - import numpy as np - - # 성능 테스트 - def performance_test(): - # 대용량 데이터 생성 - large_df = pd.DataFrame(np.random.randn(100000, 10)) - - # 성능 측정 - start_time = time.time() - atio.write(large_df, "performance_test.parquet", - format="parquet", - show_progress=True) - end_time = time.time() - - print(f"처리 시간: {end_time - start_time:.2f}초") - - # 파일 크기 확인 - import os - file_size = os.path.getsize("performance_test.parquet") - print(f"파일 크기: {file_size / 1024 / 1024:.2f} MB") \ No newline at end of file diff --git a/docs/build/html/_sources/examples.rst.txt b/docs/build/html/_sources/examples.rst.txt deleted file mode 100644 index 64cd264..0000000 --- a/docs/build/html/_sources/examples.rst.txt +++ /dev/null @@ -1,395 +0,0 @@ -사용 예제 -========= - -Atio의 다양한 사용 사례와 고급 기능을 살펴보세요. - -기본 예제 --------- - -간단한 데이터 저장 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - - # 샘플 데이터 생성 - data = { - "id": [1, 2, 3, 4, 5], - "name": ["Alice", "Bob", "Charlie", "Diana", "Eve"], - "age": [25, 30, 35, 28, 32], - "city": ["Seoul", "Busan", "Incheon", "Daegu", "Daejeon"], - "salary": [50000, 60000, 70000, 55000, 65000] - } - - df = pd.DataFrame(data) - - # 다양한 형식으로 저장 - atio.write(df, "employees.parquet", format="parquet") - atio.write(df, "employees.csv", format="csv", index=False) - atio.write(df, "employees.xlsx", format="excel", sheet_name="Employees") - -대용량 데이터 처리 ----------------- - -진행률 표시와 성능 모니터링 -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import numpy as np - import pandas as pd - import atio - - # 대용량 데이터 생성 (100만 행) - large_data = { - "id": range(1, 1000001), - "value": np.random.randn(1000000), - "category": np.random.choice(["A", "B", "C"], 1000000), - "timestamp": pd.date_range("2024-01-01", periods=1000000, freq="S") - } - - large_df = pd.DataFrame(large_data) - - # 진행률 표시와 성능 모니터링 활성화 - atio.write( - large_df, - "large_dataset.parquet", - format="parquet", - show_progress=True, - verbose=True, - compression='snappy' - ) - -데이터베이스 연동 ---------------- - -PostgreSQL 데이터베이스 저장 -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - from sqlalchemy import create_engine - - # 데이터베이스 연결 - engine = create_engine('postgresql://username:password@localhost:5432/mydb') - - # 샘플 데이터 - sales_data = { - "order_id": [1001, 1002, 1003, 1004, 1005], - "product_name": ["Laptop", "Mouse", "Keyboard", "Monitor", "Headphones"], - "quantity": [1, 2, 1, 1, 3], - "price": [1200, 25, 75, 300, 150], - "order_date": pd.date_range("2024-01-01", periods=5) - } - - sales_df = pd.DataFrame(sales_data) - - # 데이터베이스에 안전하게 저장 - atio.write( - sales_df, - format="sql", - name="sales_orders", - con=engine, - if_exists="replace", - index=False - ) - -스냅샷 기반 버전 관리 -------------------- - -데이터 버전 관리 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - from datetime import datetime, timedelta - - # 초기 데이터 - initial_data = { - "user_id": [1, 2, 3], - "name": ["Alice", "Bob", "Charlie"], - "status": ["active", "active", "inactive"] - } - - df = pd.DataFrame(initial_data) - - # 초기 스냅샷 생성 - snapshot_id_1 = atio.write_snapshot(df, "users", format="parquet") - print(f"초기 스냅샷 생성: {snapshot_id_1}") - - # 데이터 업데이트 - df.loc[df['user_id'] == 3, 'status'] = 'active' - df = df.append({"user_id": 4, "name": "Diana", "status": "active"}, ignore_index=True) - - # 업데이트된 스냅샷 생성 - snapshot_id_2 = atio.write_snapshot(df, "users", format="parquet") - print(f"업데이트 스냅샷 생성: {snapshot_id_2}") - - # 최신 데이터 읽기 - latest_df = atio.read_table("users", format="parquet") - print("최신 데이터:") - print(latest_df) - - # 특정 스냅샷 읽기 - initial_df = atio.read_table("users", snapshot_id=snapshot_id_1, format="parquet") - print("초기 데이터:") - print(initial_df) - - # 오래된 스냅샷 정리 (7일 이상) - deleted_count = atio.expire_snapshots("users", days=7, format="parquet") - print(f"삭제된 스냅샷 수: {deleted_count}") - -Polars DataFrame 활용 -------------------- - -고성능 데이터 처리 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import polars as pl - import numpy as np - - # Polars DataFrame 생성 - polars_data = { - "id": range(1, 10001), - "value": np.random.randn(10000), - "category": np.random.choice(["A", "B", "C", "D"], 10000), - "score": np.random.uniform(0, 100, 10000) - } - - polars_df = pl.DataFrame(polars_data) - - # Polars DataFrame 저장 - atio.write( - polars_df, - "polars_data.parquet", - format="parquet", - compression='snappy', - show_progress=True - ) - - # 데이터 변환 후 저장 - filtered_df = polars_df.filter(pl.col("score") > 50) - aggregated_df = filtered_df.group_by("category").agg( - pl.col("value").mean().alias("avg_value"), - pl.col("score").mean().alias("avg_score") - ) - - atio.write(aggregated_df, "aggregated_data.parquet", format="parquet") - -에러 처리 및 복구 ---------------- - -안전한 데이터 처리 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - import os - - def safe_data_processing(): - try: - # 원본 파일이 있는지 확인 - if os.path.exists("important_data.parquet"): - print("원본 파일이 존재합니다.") - - # 데이터 처리 및 저장 - df = pd.DataFrame({ - "id": [1, 2, 3], - "data": ["important", "data", "here"] - }) - - atio.write(df, "important_data.parquet", format="parquet") - print("데이터가 안전하게 저장되었습니다.") - - # SUCCESS 파일 확인 - if os.path.exists("important_data.parquet_SUCCESS"): - print("저장 완료 플래그가 생성되었습니다.") - - except Exception as e: - print(f"오류 발생: {e}") - print("원본 파일이 보존되었습니다.") - - # 임시 파일 정리 - temp_files = [f for f in os.listdir(".") if f.startswith("important_data.parquet.tmp")] - for temp_file in temp_files: - try: - os.remove(temp_file) - print(f"임시 파일 정리: {temp_file}") - except: - pass - - # 안전한 데이터 처리 실행 - safe_data_processing() - -배치 처리 -------- - -여러 파일 동시 처리 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - import os - from pathlib import Path - - def process_multiple_files(): - # 처리할 파일 목록 - files_to_process = [ - {"name": "users", "data": pd.DataFrame({"id": [1, 2], "name": ["Alice", "Bob"]})}, - {"name": "products", "data": pd.DataFrame({"id": [1, 2], "product": ["Laptop", "Mouse"]})}, - {"name": "orders", "data": pd.DataFrame({"id": [1, 2], "amount": [100, 200]})} - ] - - # 각 파일을 안전하게 처리 - for file_info in files_to_process: - try: - file_path = f"{file_info['name']}.parquet" - atio.write( - file_info['data'], - file_path, - format="parquet", - show_progress=True - ) - print(f"{file_info['name']} 파일 처리 완료") - - except Exception as e: - print(f"{file_info['name']} 파일 처리 실패: {e}") - continue - - # 배치 처리 실행 - process_multiple_files() - -성능 최적화 ---------- - -압축 및 최적화 설정 -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - import numpy as np - - # 대용량 데이터 생성 - large_df = pd.DataFrame({ - "id": range(1, 100001), - "value": np.random.randn(100000), - "text": ["sample text"] * 100000 - }) - - # 다양한 압축 설정으로 성능 비교 - compression_settings = [ - ("snappy", "fast_compression.parquet"), - ("gzip", "balanced_compression.parquet"), - ("brotli", "high_compression.parquet") - ] - - for compression, filename in compression_settings: - print(f"\n{compression} 압축으로 저장 중...") - atio.write( - large_df, - filename, - format="parquet", - compression=compression, - show_progress=True, - verbose=True - ) - - # 파일 크기 확인 - file_size = os.path.getsize(filename) / (1024 * 1024) # MB - print(f"파일 크기: {file_size:.2f} MB") - -실제 사용 사례 ------------- - -데이터 파이프라인 -~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - from datetime import datetime, timedelta - - class DataPipeline: - def __init__(self, base_path="data"): - self.base_path = Path(base_path) - self.base_path.mkdir(exist_ok=True) - - def extract_data(self): - """데이터 추출 (시뮬레이션)""" - # 실제로는 API나 데이터베이스에서 데이터를 가져옴 - data = { - "timestamp": pd.date_range(datetime.now(), periods=1000, freq="H"), - "value": np.random.randn(1000), - "source": ["api"] * 1000 - } - return pd.DataFrame(data) - - def transform_data(self, df): - """데이터 변환""" - # 시간별 집계 - df['hour'] = df['timestamp'].dt.hour - df['day'] = df['timestamp'].dt.date - - # 일별 통계 - daily_stats = df.groupby('day').agg({ - 'value': ['mean', 'std', 'min', 'max'] - }).round(2) - - return daily_stats - - def load_data(self, df, table_name): - """데이터 로드""" - # 스냅샷 생성 - snapshot_id = atio.write_snapshot( - df, - table_name, - format="parquet" - ) - - # 최신 데이터도 별도 저장 - latest_path = self.base_path / f"{table_name}_latest.parquet" - atio.write(df, str(latest_path), format="parquet") - - return snapshot_id - - def run_pipeline(self): - """파이프라인 실행""" - print("데이터 파이프라인 시작...") - - # 1. 데이터 추출 - raw_data = self.extract_data() - print(f"추출된 데이터: {len(raw_data)} 행") - - # 2. 데이터 변환 - processed_data = self.transform_data(raw_data) - print(f"처리된 데이터: {len(processed_data)} 행") - - # 3. 데이터 로드 - snapshot_id = self.load_data(processed_data, "daily_stats") - print(f"스냅샷 생성 완료: {snapshot_id}") - - # 4. 오래된 스냅샷 정리 - deleted_count = atio.expire_snapshots("daily_stats", days=30) - print(f"정리된 스냅샷: {deleted_count}개") - - print("파이프라인 완료!") - - # 파이프라인 실행 - pipeline = DataPipeline() - pipeline.run_pipeline() \ No newline at end of file diff --git a/docs/build/html/_sources/formats.rst.txt b/docs/build/html/_sources/formats.rst.txt deleted file mode 100644 index 488620c..0000000 --- a/docs/build/html/_sources/formats.rst.txt +++ /dev/null @@ -1,456 +0,0 @@ -지원하는 파일 형식 -================== - -Atio는 다양한 데이터 형식을 지원합니다. 각 형식별로 지원하는 라이브러리와 사용법을 설명합니다. - -Pandas DataFrame 형식 --------------------- - -Pandas DataFrame은 가장 많은 형식을 지원합니다. - -CSV (Comma-Separated Values) -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import pandas as pd - - df = pd.DataFrame({ - "name": ["Alice", "Bob", "Charlie"], - "age": [25, 30, 35], - "city": ["Seoul", "Busan", "Incheon"] - }) - - # 기본 CSV 저장 - atio.write(df, "users.csv", format="csv") - - # 추가 옵션과 함께 저장 - atio.write(df, "users.csv", format="csv", - index=False, - encoding='utf-8', - sep=';') - -**지원 옵션:** -- `index`: 인덱스 포함 여부 (기본값: True) -- `encoding`: 인코딩 방식 (기본값: 'utf-8') -- `sep`: 구분자 (기본값: ',') -- `header`: 헤더 포함 여부 (기본값: True) - -Parquet -~~~~~~~ - -.. code-block:: python - - # Parquet 형식으로 저장 - atio.write(df, "users.parquet", format="parquet") - - # 압축 옵션과 함께 저장 - atio.write(df, "users.parquet", format="parquet", - compression='snappy') - -**지원 옵션:** -- `compression`: 압축 방식 ('snappy', 'gzip', 'brotli', None) -- `index`: 인덱스 포함 여부 (기본값: True) - -Excel -~~~~~ - -.. code-block:: python - - # Excel 파일로 저장 - atio.write(df, "users.xlsx", format="excel") - - # 시트명과 옵션 지정 - atio.write(df, "users.xlsx", format="excel", - sheet_name="Users", - index=False) - -**지원 옵션:** -- `sheet_name`: 시트 이름 (기본값: 'Sheet1') -- `index`: 인덱스 포함 여부 (기본값: True) -- `engine`: 엔진 ('openpyxl', 'xlsxwriter') - -**필요 라이브러리:** `pip install openpyxl` - -JSON -~~~~ - -.. code-block:: python - - # JSON 형식으로 저장 - atio.write(df, "users.json", format="json") - - # 들여쓰기와 함께 저장 - atio.write(df, "users.json", format="json", - indent=2, - orient='records') - -**지원 옵션:** -- `orient`: JSON 구조 ('split', 'records', 'index', 'columns', 'values', 'table') -- `indent`: 들여쓰기 크기 -- `date_format`: 날짜 형식 - -Pickle -~~~~~~ - -.. code-block:: python - - # Pickle 형식으로 저장 - atio.write(df, "users.pkl", format="pickle") - - # 압축과 함께 저장 - atio.write(df, "users.pkl", format="pickle", - compression='gzip') - -**지원 옵션:** -- `compression`: 압축 방식 ('gzip', 'bz2', 'xz', None) - -HTML -~~~~ - -.. code-block:: python - - # HTML 테이블로 저장 - atio.write(df, "users.html", format="html") - - # 스타일과 함께 저장 - atio.write(df, "users.html", format="html", - index=False, - classes='table table-striped') - -**지원 옵션:** -- `classes`: CSS 클래스 -- `index`: 인덱스 포함 여부 (기본값: True) - -SQL -~~~ - -.. code-block:: python - - import atio - import pandas as pd - from sqlalchemy import create_engine - - # 데이터베이스 연결 - engine = create_engine('postgresql://user:password@localhost/dbname') - - df = pd.DataFrame({ - "id": [1, 2, 3], - "name": ["Alice", "Bob", "Charlie"] - }) - - # SQL 데이터베이스에 저장 - atio.write(df, format="sql", - name="users", - con=engine, - if_exists='replace') - -**지원 옵션:** -- `name`: 테이블 이름 (필수) -- `con`: 데이터베이스 연결 객체 (필수) -- `if_exists`: 테이블이 존재할 때 동작 ('fail', 'replace', 'append') -- `index`: 인덱스를 컬럼으로 저장 여부 (기본값: True) - -**필요 라이브러리:** `pip install sqlalchemy` - -Polars DataFrame 형식 --------------------- - -Polars는 빠른 데이터 처리를 위한 현대적인 DataFrame 라이브러리입니다. - -CSV -~~~ - -.. code-block:: python - - import atio - import polars as pl - - df = pl.DataFrame({ - "name": ["Alice", "Bob", "Charlie"], - "age": [25, 30, 35], - "city": ["Seoul", "Busan", "Incheon"] - }) - - # CSV 저장 - atio.write(df, "users.csv", format="csv") - - # 구분자와 함께 저장 - atio.write(df, "users.csv", format="csv", - separator=';') - -**지원 옵션:** -- `separator`: 구분자 (기본값: ',') -- `include_header`: 헤더 포함 여부 (기본값: True) - -Parquet -~~~~~~~ - -.. code-block:: python - - # Parquet 저장 - atio.write(df, "users.parquet", format="parquet") - - # 압축과 함께 저장 - atio.write(df, "users.parquet", format="parquet", - compression="snappy") - -**지원 옵션:** -- `compression`: 압축 방식 ('snappy', 'gzip', 'brotli', 'lz4raw', 'zstd', None) - -JSON -~~~~ - -.. code-block:: python - - # JSON 저장 - atio.write(df, "users.json", format="json") - - # 파일별 저장 - atio.write(df, "users.json", format="json", - file=True) - -**지원 옵션:** -- `file`: 파일별 저장 여부 (기본값: False) - -IPC (Arrow) -~~~~~~~~~~~ - -.. code-block:: python - - # IPC (Arrow) 형식으로 저장 - atio.write(df, "users.arrow", format="ipc") - - # 압축과 함께 저장 - atio.write(df, "users.arrow", format="ipc", - compression="lz4") - -**지원 옵션:** -- `compression`: 압축 방식 ('lz4', 'zstd', None) - -Avro -~~~~ - -.. code-block:: python - - # Avro 형식으로 저장 - atio.write(df, "users.avro", format="avro") - -**필요 라이브러리:** `pip install fastavro` - -Excel -~~~~~ - -.. code-block:: python - - # Excel 저장 - atio.write(df, "users.xlsx", format="excel") - -**필요 라이브러리:** `pip install xlsx2csv openpyxl` - -Database -~~~~~~~~ - -.. code-block:: python - - # 데이터베이스에 저장 - atio.write(df, format="database", - table_name="users", - connection_uri="postgresql://user:password@localhost/dbname") - -**지원 옵션:** -- `table_name`: 테이블 이름 (필수) -- `connection_uri`: 데이터베이스 연결 URI (필수) - -**필요 라이브러리:** `pip install connectorx` - -NumPy 배열 형식 --------------- - -NumPy 배열은 수치 데이터 처리에 최적화된 형식을 지원합니다. - -NPY (NumPy Binary) -~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import atio - import numpy as np - - arr = np.array([[1, 2, 3], [4, 5, 6]]) - - # .npy 파일로 저장 - atio.write(arr, "array.npy", format="npy") - -**특징:** -- 단일 배열을 효율적으로 저장 -- 메타데이터와 함께 저장 -- 빠른 읽기/쓰기 속도 - -NPZ (NumPy Compressed) -~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 여러 배열을 딕셔너리로 저장 - arrays = { - "features": np.random.randn(1000, 10), - "labels": np.random.randint(0, 2, 1000), - "metadata": np.array([1, 2, 3, 4, 5]) - } - - # 압축되지 않은 .npz 파일로 저장 - atio.write(arrays, "data.npz", format="npz") - - # 압축된 .npz 파일로 저장 - atio.write(arrays, "data.npz", format="npz_compressed") - -**특징:** -- 여러 배열을 하나의 파일에 저장 -- 압축 옵션으로 저장 공간 절약 -- 딕셔너리 형태로 데이터 구조화 - -CSV -~~~ - -.. code-block:: python - - # CSV로 저장 - atio.write(arr, "array.csv", format="csv") - - # 구분자와 함께 저장 - atio.write(arr, "array.csv", format="csv", - delimiter=';', - fmt='%.2f') - -**지원 옵션:** -- `delimiter`: 구분자 (기본값: ',') -- `fmt`: 숫자 형식 (예: '%.2f', '%.4e') -- `header`: 헤더 포함 여부 -- `comments`: 주석 문자 - -형식별 성능 비교 ----------------- - -다양한 형식의 성능 특성을 비교해보겠습니다. - -속도 비교 -~~~~~~~~~ - -1. **가장 빠른 형식:** - - NumPy: `.npy`, `.npz` - - Polars: `.ipc` (Arrow) - - Pandas: `.parquet` (snappy 압축) - -2. **중간 속도:** - - CSV (단순한 구조) - - JSON (중간 복잡도) - -3. **상대적으로 느린 형식:** - - Excel (복잡한 구조) - - Pickle (Python 특화) - -용량 비교 -~~~~~~~~~ - -1. **가장 작은 용량:** - - `.parquet` (컬럼 기반 압축) - - `.npz_compressed` (압축된 NumPy) - - `.ipc` (Arrow 압축) - -2. **중간 용량:** - - `.npy` (단일 배열) - - JSON (텍스트 기반) - -3. **상대적으로 큰 용량:** - - CSV (텍스트 기반) - - Excel (복잡한 구조) - -호환성 비교 -~~~~~~~~~~~ - -1. **최고 호환성:** - - CSV (모든 시스템에서 지원) - - JSON (웹 표준) - -2. **좋은 호환성:** - - Excel (비즈니스 환경) - - Parquet (빅데이터 생태계) - -3. **제한적 호환성:** - - `.npy`/`.npz` (Python/NumPy 특화) - - `.ipc` (Arrow 생태계) - -권장 사용 사례 -------------- - -데이터 분석 및 머신러닝 -~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 학습 데이터: 빠른 읽기/쓰기를 위해 Parquet 사용 - atio.write(training_data, "train.parquet", format="parquet") - - # 모델 가중치: NumPy 배열로 저장 - atio.write(model_weights, "weights.npy", format="npy") - - # 실험 결과: JSON으로 저장 (가독성) - atio.write(results, "experiment_results.json", format="json") - -웹 애플리케이션 -~~~~~~~~~~~~~~ - -.. code-block:: python - - # API 응답: JSON 형식 - atio.write(api_data, "response.json", format="json") - - # 대용량 데이터: Parquet 형식 - atio.write(large_dataset, "dataset.parquet", format="parquet") - -데이터 파이프라인 -~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # 중간 결과: 빠른 처리를 위해 IPC 사용 - atio.write(intermediate_data, "step1.arrow", format="ipc") - - # 최종 결과: 호환성을 위해 CSV 사용 - atio.write(final_data, "output.csv", format="csv") - -비즈니스 보고서 -~~~~~~~~~~~~~~ - -.. code-block:: python - - # 엑셀 보고서 - atio.write(report_data, "monthly_report.xlsx", format="excel") - - # HTML 대시보드 - atio.write(dashboard_data, "dashboard.html", format="html") - -형식 확장하기 ------------- - -새로운 형식을 추가하려면 플러그인 시스템을 사용하세요. - -.. code-block:: python - - from atio.plugins import register_writer - import pandas as pd - - # 커스텀 형식 등록 - def yaml_writer(df, path, **kwargs): - import yaml - data = df.to_dict('records') - with open(path, 'w') as f: - yaml.dump(data, f, **kwargs) - - # 등록 - register_writer(pd.DataFrame, "yaml", yaml_writer) - - # 사용 - atio.write(df, "data.yaml", format="yaml") \ No newline at end of file diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt deleted file mode 100644 index b078eb6..0000000 --- a/docs/build/html/_sources/index.rst.txt +++ /dev/null @@ -1,165 +0,0 @@ -Atio Documentation -================== - -**Atio** 🛡️는 안전하고 원자적인 파일 쓰기를 지원하는 경량 Python 라이브러리입니다. - -Pandas, Polars, NumPy 등 데이터 객체 저장 시 **파일 손상 없이**, **트랜잭션처럼 안전하게 처리**할 수 있습니다. - -주요 기능 ---------- - -🔒 **Atomic File Writing** - Safe writing using temporary files - -📊 **Multiple Format Support** - CSV, Parquet, Excel, JSON, etc. - -🗄️ **Database Support** - Direct SQL and Database writing - -📈 **Progress Display** - Progress monitoring for large data processing - -🔄 **Rollback Function** - Automatic recovery when errors occur - -🎯 **Simple API** - Intuitive and easy-to-use interface - -📋 **Version Management** - Snapshot-based data version management - -🧹 **Auto Cleanup** - Automatic deletion of old data - -빠른 시작 ---------- - -.. code-block:: python - - import atio - import pandas as pd - - # 간단한 DataFrame 생성 - df = pd.DataFrame({ - "name": ["Alice", "Bob", "Charlie"], - "age": [25, 30, 35], - "city": ["Seoul", "Busan", "Incheon"] - }) - - # 안전한 파일 쓰기 - atio.write(df, "users.parquet", format="parquet") - - # 진행도 표시와 함께 저장 - atio.write(df, "users.csv", format="csv", show_progress=True) - -지원하는 형식 ------------- - -.. list-table:: 지원하는 파일 형식 - :widths: 20 20 20 20 - :header-rows: 1 - - * - 형식 - - Pandas - - Polars - - NumPy - * - CSV - - ✅ - - ✅ - - ✅ - * - Parquet - - ✅ - - ✅ - - ❌ - * - Excel - - ✅ - - ✅ - - ❌ - * - JSON - - ✅ - - ✅ - - ❌ - * - Pickle - - ✅ - - ❌ - - ❌ - * - HTML - - ✅ - - ❌ - - ❌ - * - SQL - - ✅ - - ❌ - - ❌ - * - Database - - ❌ - - ✅ - - ❌ - * - NPY/NPZ - - ❌ - - ❌ - - ✅ - -사용 사례 --------- - -🔹 **데이터 파이프라인** - ETL 과정에서 중간 데이터 안전하게 저장 - -🔹 **실험 데이터 관리** - 머신러닝 실험 결과의 버전 관리 - -🔹 **대용량 데이터 처리** - 대용량 파일의 안전한 저장 및 진행도 모니터링 - -🔹 **데이터베이스 연동** - Pandas/Polars 데이터를 SQL/NoSQL DB에 안전하게 저장 - -목차 ----- - -.. toctree:: - :maxdepth: 2 - :caption: 사용자 가이드: - - installation - quickstart - examples - advanced_usage - -.. toctree:: - :maxdepth: 2 - :caption: API 참조: - - api - configuration - -API Reference -============= - -핵심 함수들 ------------ - -.. automodule:: atio - :members: - :undoc-members: - -.. automodule:: atio.core - :members: - :undoc-members: - -.. automodule:: atio.plugins - :members: - :undoc-members: - -.. automodule:: atio.utils - :members: - :undoc-members: - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/build/html/_sources/installation.rst.txt b/docs/build/html/_sources/installation.rst.txt deleted file mode 100644 index eb0162b..0000000 --- a/docs/build/html/_sources/installation.rst.txt +++ /dev/null @@ -1,199 +0,0 @@ -설치 가이드 -========== - -Atio를 설치하는 방법을 안내합니다. - -PyPI를 통한 설치 ----------------- - -가장 간단한 설치 방법은 PyPI를 사용하는 것입니다: - -.. code-block:: bash - - pip install atio - -특정 버전 설치 -~~~~~~~~~~~~~ - -특정 버전을 설치하려면: - -.. code-block:: bash - - pip install atio==2.0.0 - -개발 버전 설치 -~~~~~~~~~~~~~ - -최신 개발 버전을 설치하려면: - -.. code-block:: bash - - pip install git+https://github.com/seojaeohcode/atio.git - -의존성 ------- - -필수 의존성 -~~~~~~~~~~ - -- Python 3.8+ -- pandas -- numpy - -선택적 의존성 -~~~~~~~~~~~~ - -특정 형식을 사용하려면 추가 라이브러리가 필요합니다: - -**Parquet 형식:** -.. code-block:: bash - - pip install pyarrow - # 또는 - pip install fastparquet - -**Excel 형식:** -.. code-block:: bash - - pip install openpyxl - # 또는 - pip install xlsxwriter - -**SQL 데이터베이스:** -.. code-block:: bash - - pip install sqlalchemy - -**Polars 지원:** -.. code-block:: bash - - pip install polars - -**Avro 형식 (Polars):** -.. code-block:: bash - - pip install fastavro - -**Polars Excel 지원:** -.. code-block:: bash - - pip install xlsx2csv openpyxl - -**Polars 데이터베이스 지원:** -.. code-block:: bash - - pip install connectorx - -모든 의존성 설치 -~~~~~~~~~~~~~~~ - -모든 기능을 사용하려면: - -.. code-block:: bash - - pip install atio[all] - -또는 개별적으로: - -.. code-block:: bash - - pip install atio - pip install pyarrow openpyxl sqlalchemy polars fastavro xlsx2csv connectorx - -환경 확인 ---------- - -설치가 완료되었는지 확인하려면: - -.. code-block:: python - - import atio - print(f"Atio 버전: {atio.__version__}") - - # 기본 기능 테스트 - import pandas as pd - df = pd.DataFrame({"test": [1, 2, 3]}) - atio.write(df, "test.parquet", format="parquet") - print("설치가 성공적으로 완료되었습니다!") - -가상환경 사용 권장 ------------------ - -프로젝트별로 독립적인 환경을 유지하기 위해 가상환경 사용을 권장합니다: - -.. code-block:: bash - - # 가상환경 생성 - python -m venv atio_env - - # 가상환경 활성화 (Windows) - atio_env\Scripts\activate - - # 가상환경 활성화 (macOS/Linux) - source atio_env/bin/activate - - # Atio 설치 - pip install atio - -Conda 사용 ---------- - -Conda를 사용하는 경우: - -.. code-block:: bash - - # Conda 환경 생성 - conda create -n atio_env python=3.8 - conda activate atio_env - - # Atio 설치 - pip install atio - -문제 해결 ---------- - -설치 중 문제가 발생하는 경우: - -**권한 오류:** -.. code-block:: bash - - pip install atio --user - -**캐시 문제:** -.. code-block:: bash - - pip install atio --no-cache-dir - -**의존성 충돌:** -.. code-block:: bash - - pip install atio --force-reinstall - -**특정 Python 버전:** -.. code-block:: bash - - python3.8 -m pip install atio - -업그레이드 ----------- - -최신 버전으로 업그레이드: - -.. code-block:: bash - - pip install --upgrade atio - -특정 버전으로 다운그레이드: - -.. code-block:: bash - - pip install atio==1.0.0 - -제거 ----- - -Atio를 제거하려면: - -.. code-block:: bash - - pip uninstall atio \ No newline at end of file diff --git a/docs/build/html/_sources/quickstart.rst.txt b/docs/build/html/_sources/quickstart.rst.txt deleted file mode 100644 index 12b7060..0000000 --- a/docs/build/html/_sources/quickstart.rst.txt +++ /dev/null @@ -1,126 +0,0 @@ -빠른 시작 -========== - -Atio를 사용하여 안전한 파일 쓰기를 시작해보세요. - -기본 사용법 ----------- - -가장 간단한 사용법부터 시작해보겠습니다: - -.. code-block:: python - - import atio - import pandas as pd - - # 샘플 데이터 생성 - df = pd.DataFrame({ - "name": ["Alice", "Bob", "Charlie"], - "age": [25, 30, 35], - "city": ["Seoul", "Busan", "Incheon"] - }) - - # 안전한 파일 쓰기 - atio.write(df, "users.parquet", format="parquet") - -이 코드는 다음과 같은 안전장치를 제공합니다: - -- ✅ 임시 파일에 먼저 저장 -- ✅ 저장 완료 후 원자적으로 파일 교체 -- ✅ 실패 시 원본 파일 보존 -- ✅ `_SUCCESS` 플래그 파일 생성 - -다양한 형식 지원 ---------------- - -Atio는 다양한 데이터 형식을 지원합니다: - -.. code-block:: python - - # CSV 형식 - atio.write(df, "users.csv", format="csv", index=False) - - # Excel 형식 - atio.write(df, "users.xlsx", format="excel", sheet_name="Users") - - # JSON 형식 - atio.write(df, "users.json", format="json", orient="records") - - # Parquet 형식 (권장) - atio.write(df, "users.parquet", format="parquet") - -진행률 표시 ----------- - -대용량 데이터 처리 시 진행률을 확인할 수 있습니다: - -.. code-block:: python - - # 진행률 표시 활성화 - atio.write(large_df, "big_data.parquet", format="parquet", show_progress=True) - -성능 모니터링 ------------- - -상세한 성능 정보를 확인하려면: - -.. code-block:: python - - # 성능 정보 출력 - atio.write(df, "data.parquet", format="parquet", verbose=True) - -Polars DataFrame 지원 -------------------- - -Polars DataFrame도 지원합니다: - -.. code-block:: python - - import polars as pl - - # Polars DataFrame 생성 - polars_df = pl.DataFrame({ - "a": [1, 2, 3], - "b": [4, 5, 6] - }) - - # Polars DataFrame 저장 - atio.write(polars_df, "data.parquet", format="parquet") - -데이터베이스 저장 ---------------- - -SQL 데이터베이스에 직접 저장할 수도 있습니다: - -.. code-block:: python - - from sqlalchemy import create_engine - - # 데이터베이스 연결 - engine = create_engine('postgresql://user:password@localhost/dbname') - - # 데이터베이스에 저장 - atio.write(df, format="sql", name="users", con=engine, if_exists="replace") - -스냅샷 기능 ----------- - -데이터 버전 관리를 위한 스냅샷 기능: - -.. code-block:: python - - # 스냅샷 생성 - atio.write_snapshot(df, "users", format="parquet") - - # 스냅샷 읽기 - df = atio.read_table("users", snapshot_id="latest") - - # 오래된 스냅샷 정리 - atio.expire_snapshots("users", days=30) - -다음 단계 --------- - -- :doc:`api` - 전체 API 참조 -- :doc:`examples` - 고급 사용 예제 -- :doc:`installation` - 설치 가이드 \ No newline at end of file diff --git a/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js deleted file mode 100644 index 8141580..0000000 --- a/docs/build/html/_static/_sphinx_javascript_frameworks_compat.js +++ /dev/null @@ -1,123 +0,0 @@ -/* Compatability shim for jQuery and underscores.js. - * - * Copyright Sphinx contributors - * Released under the two clause BSD licence - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - 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- -/** - * Simple result scoring code. - */ -if (typeof Scorer === "undefined") { - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [docname, title, anchor, descr, score, filename] - // and returns the new score. - /* - score: result => { - const [docname, title, anchor, descr, score, filename, kind] = result - return score - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2, - }; -} - -// Global search result kind enum, used by themes to style search results. -class SearchResultKind { - static get index() { return "index"; } - static get object() { return "object"; } - static get text() { return "text"; } - static get title() { return "title"; } -} - -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); -}; - -/** - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping - */ -const _escapeRegExp = (string) => - string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string - -const _displayItem = (item, searchTerms, highlightTerms) => { - const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; - const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; - const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - const contentRoot = document.documentElement.dataset.content_root; - - const [docName, title, anchor, descr, score, _filename, kind] = item; - - let listItem = document.createElement("li"); - // Add a class representing the item's type: - // can be used by a theme's CSS selector for styling - // See SearchResultKind for the class names. - listItem.classList.add(`kind-${kind}`); - let requestUrl; - let linkUrl; - if (docBuilder === "dirhtml") { - // dirhtml builder - let dirname = docName + "/"; - if (dirname.match(/\/index\/$/)) - dirname = dirname.substring(0, dirname.length - 6); - else if (dirname === "index/") dirname = ""; - requestUrl = contentRoot + dirname; - linkUrl = requestUrl; - } else { - // normal html builders - requestUrl = contentRoot + docName + docFileSuffix; - linkUrl = docName + docLinkSuffix; - } - let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + anchor; - linkEl.dataset.score = score; - linkEl.innerHTML = title; - if (descr) { - listItem.appendChild(document.createElement("span")).innerHTML = - " (" + descr + ")"; - // highlight search terms in the description - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - } - else if (showSearchSummary) - fetch(requestUrl) - .then((responseData) => responseData.text()) - .then((data) => { - if (data) - listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, anchor) - ); - // highlight search terms in the summary - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = Documentation.ngettext( - "Search finished, found one page matching the search query.", - "Search finished, found ${resultCount} pages matching the search query.", - resultCount, - ).replace('${resultCount}', resultCount); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms, - highlightTerms, -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms, highlightTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; -// Helper function used by query() to order search results. -// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. -// Order the results by score (in opposite order of appearance, since the -// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. -const _orderResultsByScoreThenName = (a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString, anchor) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - for (const removalQuery of [".headerlink", "script", "style"]) { - htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); - } - if (anchor) { - const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); - if (anchorContent) return anchorContent.textContent; - - console.warn( - `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` - ); - } - - // if anchor not specified or not found, fall back to main content - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent) return docContent.textContent; - - console.warn( - "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.setAttribute("role", "list"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - _parseQuery: (query) => { - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; - }, - - /** - * execute search (requires search index to be loaded) - */ - _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // Collect multiple result groups to be sorted separately and then ordered. - // Each is an array of [docname, title, anchor, descr, score, filename, kind]. - const normalResults = []; - const nonMainIndexResults = []; - - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase().trim(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - const score = Math.round(Scorer.title * queryLower.length / title.length); - const boost = titles[file] === title ? 1 : 0; // add a boost for document titles - normalResults.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score + boost, - filenames[file], - SearchResultKind.title, - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id, isMain] of foundEntries) { - const score = Math.round(100 * queryLower.length / entry.length); - const result = [ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - SearchResultKind.index, - ]; - if (isMain) { - normalResults.push(result); - } else { - nonMainIndexResults.push(result); - } - } - } - } - - // lookup as object - objectTerms.forEach((term) => - normalResults.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) { - normalResults.forEach((item) => (item[4] = Scorer.score(item))); - nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); - } - - // Sort each group of results by score and then alphabetically by name. - normalResults.sort(_orderResultsByScoreThenName); - nonMainIndexResults.sort(_orderResultsByScoreThenName); - - // Combine the result groups in (reverse) order. - // Non-main index entries are typically arbitrary cross-references, - // so display them after other results. - let results = [...nonMainIndexResults, ...normalResults]; - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - return results.reverse(); - }, - - query: (query) => { - const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); - const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms, highlightTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - SearchResultKind.object, - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - // find documents, if any, containing the query word in their text/title term indices - // use Object.hasOwnProperty to avoid mismatching against prototype properties - const arr = [ - { files: terms.hasOwnProperty(word) ? terms[word] : undefined, score: Scorer.term }, - { files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined, score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - if (!terms.hasOwnProperty(word)) { - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - } - if (!titleTerms.hasOwnProperty(word)) { - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); - }); - } - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, new Map()); - const fileScores = scoreMap.get(file); - fileScores.set(word, record.score); - }); - }); - - // create the mapping - files.forEach((file) => { - if (!fileMap.has(file)) fileMap.set(file, [word]); - else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file).get(w))); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - SearchResultKind.text, - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords, anchor) => { - const text = Search.htmlToText(htmlText, anchor); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/docs/build/html/_static/sphinx_highlight.js b/docs/build/html/_static/sphinx_highlight.js deleted file mode 100644 index 8a96c69..0000000 --- a/docs/build/html/_static/sphinx_highlight.js +++ /dev/null @@ -1,154 +0,0 @@ -/* Highlighting utilities for Sphinx HTML documentation. */ -"use strict"; - -const SPHINX_HIGHLIGHT_ENABLED = true - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - const rest = document.createTextNode(val.substr(pos + text.length)); - parent.insertBefore( - span, - parent.insertBefore( - rest, - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - /* There may be more occurrences of search term in this node. So call this - * function recursively on the remaining fragment. - */ - _highlight(rest, addItems, text, className); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - -/** - * Small JavaScript module for the documentation. - */ -const SphinxHighlight = { - - /** - * highlight the search words provided in localstorage in the text - */ - highlightSearchWords: () => { - if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight - - // get and clear terms from localstorage - const url = new URL(window.location); - const highlight = - localStorage.getItem("sphinx_highlight_terms") - || url.searchParams.get("highlight") - || ""; - localStorage.removeItem("sphinx_highlight_terms") - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - - // get individual terms from highlight string - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - _("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(() => { - /* Do not call highlightSearchWords() when we are on the search page. - * It will highlight words from the *previous* search query. - */ - if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); - SphinxHighlight.initEscapeListener(); -}); diff --git a/docs/build/html/_static/translations.js b/docs/build/html/_static/translations.js deleted file mode 100644 index 58fdf54..0000000 --- a/docs/build/html/_static/translations.js +++ /dev/null @@ -1,62 +0,0 @@ -Documentation.addTranslations({ - "locale": "ko", - "messages": { - "%(filename)s — %(docstitle)s": "%(filename)s — %(docstitle)s", - "© %(copyright_prefix)s %(copyright)s.": "", - ", in ": ", \ubb38\uc11c - ", - "About these documents": "\uc774 \ubb38\uc11c \uc815\ubcf4", - "Automatically generated list of changes in version %(version)s": "\ubc84\uc804 %(version)s\uc758 \ubcc0\uacbd \uc0ac\ud56d (\uc790\ub3d9\uc73c\ub85c \uc0dd\uc131\ub41c \ubaa9\ub85d)", - "C API changes": "C API \ubcc0\uacbd \uc0ac\ud56d", - "Changes in Version %(version)s — %(docstitle)s": "\ubc84\uc804 %(version)s\uc758 \ubcc0\uacbd \uc0ac\ud56d — %(docstitle)s", - "Collapse sidebar": "\uc0ac\uc774\ub4dc\ubc14 \ub2eb\uae30", - "Complete Table of Contents": "\uc885\ud569 \ubaa9\ucc28", - "Contents": "\ub0b4\uc6a9", - "Copyright": "\uc800\uc791\uad8c", - "Created using Sphinx %(sphinx_version)s.": "Sphinx %(sphinx_version)s \ubc84\uc804\uc73c\ub85c \uc0dd\uc131\ub418\uc5c8\uc2b5\ub2c8\ub2e4.", - "Expand sidebar": "\uc0ac\uc774\ub4dc\ubc14 \uc5f4\uae30", - "Full index on one page": "\ud55c \ud398\uc774\uc9c0\uc5d0 \uc804\uccb4 \uc0c9\uc778 \ubcf4\uae30", - "General Index": "\uc804\uccb4 \uc0c9\uc778", - "Global Module Index": "\ubaa8\ub4c8 \ucd1d \uc0c9\uc778", - "Go": "\uc774\ub3d9", - "Hide Search Matches": "\uac80\uc0c9 \uc77c\uce58 \uc228\uae30\uae30", - "Index": "\uc0c9\uc778", - "Index – %(key)s": "", - "Index pages by letter": "\uc54c\ud30c\ubcb3\ubcc4 \uc0c9\uc778", - "Indices and tables:": "\uc0c9\uc778 \ubc0f \ud45c \ubaa9\ub85d:", - "Last updated on %(last_updated)s.": "\ucd5c\uc885 \uc5c5\ub370\uc774\ud2b8: %(last_updated)s", - "Library changes": "\ub77c\uc774\ube0c\ub7ec\ub9ac \ubcc0\uacbd \uc0ac\ud56d", - "Navigation": "\ud0d0\uc0c9", - "Next topic": "\ub2e4\uc74c \ud56d\ubaa9", - "Other changes": "\ub2e4\ub978 \ubcc0\uacbd \uc0ac\ud56d", - "Overview": "\uac1c\uc694", - "Please activate JavaScript to enable the search\n functionality.": "\uac80\uc0c9 \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\ub824\uba74 JavaScript\ub97c \ud65c\uc131\ud654\ud558\uc2ed\uc2dc\uc624.", - "Preparing search...": "\uac80\uc0c9 \uc900\ube44 \uc911\u2026", - "Previous topic": "\uc774\uc804 \ud56d\ubaa9", - "Quick search": "\ube60\ub978 \uac80\uc0c9", - "Search": "\uac80\uc0c9", - "Search Page": "\uac80\uc0c9 \ud398\uc774\uc9c0", - "Search Results": "\uac80\uc0c9 \uacb0\uacfc", - "Search finished, found one page matching the search query.": [ - "" - ], - "Search within %(docstitle)s": "%(docstitle)s\uc5d0\uc11c \ucc3e\uae30", - "Searching": "\uac80\uc0c9 \uc911", - "Searching for multiple words only shows matches that contain\n all words.": "\uc5ec\ub7ec \ub2e8\uc5b4\ub97c \uac80\uc0c9\ud558\uba74 \ubaa8\ub4e0 \ub2e8\uc5b4\uac00 \ud3ec\ud568\ub41c \uc77c\uce58 \ud56d\ubaa9\ub9cc \ud45c\uc2dc\ub429\ub2c8\ub2e4.", - "Show Source": "\uc18c\uc2a4 \ubcf4\uae30", - "Table of Contents": "\ubaa9\ucc28", - "This Page": "\ud604\uc7ac \ubb38\uc11c", - "Welcome! This is": "\ud658\uc601\ud569\ub2c8\ub2e4!", - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories.": "\uac80\uc0c9\uc5b4\uc640 \uc77c\uce58\ud558\ub294 \ubb38\uc11c\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \ubaa8\ub4e0 \ub2e8\uc5b4\uc758 \ucca0\uc790\uac00 \uc62c\ubc14\ub978\uc9c0, \ucda9\ubd84\ud55c \uce74\ud14c\uace0\ub9ac\ub97c \uc120\ud0dd\ud588\ub294\uc9c0 \ud655\uc778\ud558\uc2ed\uc2dc\uc624.", - "all functions, classes, terms": "\ud568\uc218, \ud074\ub798\uc2a4 \ubc0f \uc6a9\uc5b4 \uac1c\uad00", - "can be huge": "\ud070 \uacbd\uc6b0\uac00 \uc788\uc73c\ubbc0\ub85c \uc8fc\uc758", - "last updated": "\ucd5c\uc885 \uc5c5\ub370\uc774\ud2b8", - "lists all sections and subsections": "\ubaa8\ub4e0 \uad6c\uc5ed\uacfc \ud558\uc704 \uad6c\uc5ed \ubaa9\ub85d", - "next chapter": "\ub2e4\uc74c \uc7a5", - "previous chapter": "\uc774\uc804 \uc7a5", - "quick access to all modules": "\ubaa8\ub4e0 \ubaa8\ub4c8 \uc870\uacac\ud45c", - "search": "\uac80\uc0c9", - "search this documentation": "\ubb38\uc11c \uac80\uc0c9", - "the documentation for": "\ubb38\uc11c:" - }, - "plural_expr": "0" -}); \ No newline at end of file diff --git a/docs/build/html/advanced_usage.html b/docs/build/html/advanced_usage.html deleted file mode 100644 index ab40641..0000000 --- a/docs/build/html/advanced_usage.html +++ /dev/null @@ -1,454 +0,0 @@ - - - - - - - - - 고급 사용법 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

고급 사용법

-

이 섹션에서는 Atio의 고급 기능들을 다룹니다.

-
-

스냅샷 기반 버전 관리

-

Atio는 데이터의 버전을 관리할 수 있는 스냅샷 시스템을 제공합니다.

-
-

기본 스냅샷 쓰기

-
import atio
-import pandas as pd
-
-# 데이터 생성
-df = pd.DataFrame({
-    "id": [1, 2, 3],
-    "name": ["Alice", "Bob", "Charlie"]
-})
-
-# 스냅샷으로 저장 (버전 관리)
-atio.write_snapshot(df, "users_table", format="parquet")
-
-# 새로운 데이터로 업데이트
-df_new = pd.DataFrame({
-    "id": [1, 2, 3, 4],
-    "name": ["Alice", "Bob", "Charlie", "David"]
-})
-
-# append 모드로 스냅샷 추가
-atio.write_snapshot(df_new, "users_table", mode="append", format="parquet")
-
-
-
-
-

스냅샷 읽기

-
# 최신 버전 읽기
-latest_data = atio.read_table("users_table")
-
-# 특정 버전 읽기
-version_1_data = atio.read_table("users_table", version=1)
-
-# Polars로 읽기
-polars_data = atio.read_table("users_table", output_as="polars")
-
-
-
-
-

스냅샷 정리

-
from datetime import timedelta
-
-# 7일 이상 된 스냅샷 삭제 (dry run)
-atio.expire_snapshots("users_table", keep_for=timedelta(days=7), dry_run=True)
-
-# 실제 삭제 실행
-atio.expire_snapshots("users_table", keep_for=timedelta(days=7), dry_run=False)
-
-
-
-
-
-

데이터베이스 연동

-

Pandas와 Polars를 사용하여 데이터베이스에 안전하게 데이터를 저장할 수 있습니다.

-
-

Pandas SQL 연동

-
import atio
-import pandas as pd
-from sqlalchemy import create_engine
-
-# 데이터베이스 연결
-engine = create_engine('postgresql://user:password@localhost/dbname')
-
-df = pd.DataFrame({
-    "id": [1, 2, 3],
-    "name": ["Alice", "Bob", "Charlie"]
-})
-
-# SQL 데이터베이스에 저장
-atio.write(df, format="sql", name="users", con=engine)
-
-
-
-
-

Polars 데이터베이스 연동

-
import atio
-import polars as pl
-
-df = pl.DataFrame({
-    "id": [1, 2, 3],
-    "name": ["Alice", "Bob", "Charlie"]
-})
-
-# 데이터베이스에 저장
-atio.write(df, format="database",
-           table_name="users",
-           connection_uri="postgresql://user:password@localhost/dbname")
-
-
-
-
-
-

성능 최적화

-
-

진행도 표시

-

대용량 파일 처리 시 진행 상황을 실시간으로 확인할 수 있습니다.

-
import atio
-import pandas as pd
-import numpy as np
-
-# 대용량 데이터 생성
-large_df = pd.DataFrame(np.random.randn(1000000, 10))
-
-# 진행도 표시와 함께 저장
-atio.write(large_df, "large_data.parquet",
-           format="parquet",
-           show_progress=True)
-
-
-
-
-

상세 로깅

-

성능 진단을 위한 상세한 로깅을 활성화할 수 있습니다.

-
# 상세한 성능 정보 출력
-atio.write(df, "data.parquet", format="parquet", verbose=True)
-
-
-

이를 통해 다음과 같은 정보를 확인할 수 있습니다:

-
    -

  • 각 단계별 소요 시간

    • -

  • 임시 파일 생성 및 교체 과정

    • -

  • 백업 및 롤백 과정

    • -

  • 성능 병목점 분석

    • -
-
-
-
-

에러 처리

-

Atio는 다양한 에러 상황에 대해 안전하게 처리합니다.

-
-

파일 시스템 에러

-
import atio
-import pandas as pd
-
-df = pd.DataFrame({"a": [1, 2, 3]})
-
-try:
-    # 권한이 없는 디렉토리에 저장 시도
-    atio.write(df, "/root/data.parquet", format="parquet")
-except PermissionError as e:
-    print(f"권한 에러: {e}")
-    # 원본 파일은 그대로 보존됨
-
-
-
-
-

포맷 에러

-
try:
-    # 지원하지 않는 포맷 사용
-    atio.write(df, "data.unknown", format="unknown")
-except ValueError as e:
-    print(f"지원하지 않는 포맷: {e}")
-
-
-
-
-

데이터베이스 에러

-
try:
-    # 잘못된 데이터베이스 연결 정보
-    atio.write(df, format="sql",
-               name="users",
-               con="invalid_connection")
-except Exception as e:
-    print(f"데이터베이스 에러: {e}")
-
-
-
-
-
-

플러그인 확장

-

Atio는 플러그인 아키텍처를 통해 새로운 형식을 쉽게 추가할 수 있습니다.

-
-

커스텀 형식 등록

-
from atio.plugins import register_writer
-import pandas as pd
-
-# 커스텀 형식 등록
-def custom_writer(df, path, **kwargs):
-    # 커스텀 저장 로직
-    with open(path, 'w') as f:
-        f.write("Custom format\n")
-        f.write(df.to_string())
-
-# Pandas DataFrame에 대한 커스텀 형식 등록
-register_writer(pd.DataFrame, "custom", custom_writer)
-
-# 사용
-df = pd.DataFrame({"a": [1, 2, 3]})
-atio.write(df, "data.custom", format="custom")
-
-
-
-
-
-

NumPy 배열 처리

-

NumPy 배열의 다양한 저장 방식을 지원합니다.

-
-

단일 배열 저장

-
import atio
-import numpy as np
-
-arr = np.array([[1, 2, 3], [4, 5, 6]])
-
-# .npy 파일로 저장
-atio.write(arr, "array.npy", format="npy")
-
-# .csv 파일로 저장
-atio.write(arr, "array.csv", format="csv")
-
-
-
-
-

여러 배열 저장

-
# 여러 배열을 딕셔너리로 저장
-arrays = {
-    "features": np.random.randn(1000, 10),
-    "labels": np.random.randint(0, 2, 1000),
-    "metadata": np.array([1, 2, 3, 4, 5])
-}
-
-# 압축된 .npz 파일로 저장
-atio.write(arrays, "data.npz", format="npz_compressed")
-
-
-
-
-
-

실제 사용 사례

-
-

머신러닝 파이프라인

-
import atio
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-
-# 데이터 로드 및 전처리
-df = pd.read_csv("raw_data.csv")
-
-# 전처리된 데이터를 안전하게 저장
-atio.write(df, "processed_data.parquet", format="parquet")
-
-# 학습/테스트 분할
-X_train, X_test, y_train, y_test = train_test_split(
-    df.drop('target', axis=1), df['target'], test_size=0.2
-)
-
-# 분할된 데이터를 스냅샷으로 저장
-atio.write_snapshot(X_train, "training_data", format="parquet")
-atio.write_snapshot(X_test, "test_data", format="parquet")
-
-# 모델 학습
-model = RandomForestClassifier()
-model.fit(X_train, y_train)
-
-# 예측 결과를 안전하게 저장
-predictions = model.predict(X_test)
-results_df = pd.DataFrame({
-    'actual': y_test,
-    'predicted': predictions
-})
-
-atio.write(results_df, "predictions.parquet", format="parquet")
-
-
-
-
-

ETL 파이프라인

-
import atio
-import pandas as pd
-from sqlalchemy import create_engine
-
-# 1. 원본 데이터 로드
-raw_data = pd.read_csv("source_data.csv")
-
-# 2. 데이터 정제
-cleaned_data = raw_data.dropna()
-cleaned_data = cleaned_data[cleaned_data['value'] > 0]
-
-# 3. 안전하게 정제된 데이터 저장
-atio.write(cleaned_data, "cleaned_data.parquet", format="parquet")
-
-# 4. 데이터베이스에 저장
-engine = create_engine('postgresql://user:password@localhost/warehouse')
-atio.write(cleaned_data, format="sql",
-           name="processed_table",
-           con=engine,
-           if_exists='replace')
-
-# 5. 스냅샷으로 버전 관리
-atio.write_snapshot(cleaned_data, "daily_processed", format="parquet")
-
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/api.html b/docs/build/html/api.html deleted file mode 100644 index b4782d6..0000000 --- a/docs/build/html/api.html +++ /dev/null @@ -1,489 +0,0 @@ - - - - - - - - - API 참조 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

API 참조

-

Atio의 모든 함수와 클래스에 대한 상세한 API 문서입니다.

-
-

주요 함수

-

Atio: 안전한 원자적 파일 쓰기 라이브러리

-
-
-

Core 모듈

-

progress 적용 후 write 함수

-
-
-atio.core.write(obj, target_path=None, format=None, show_progress=False, verbose=False, **kwargs)[소스]
-

데이터 객체(obj)를 안전하게 target_path 또는 데이터베이스에 저장합니다.

-
    -

  • 파일 기반 쓰기 (format: ‘csv’, ‘parquet’, ‘excel’ 등): -- target_path (str): 필수. 데이터가 저장될 파일 경로입니다. -- 롤백 기능이 있는 원자적 쓰기를 수행합니다.

    • -

  • 데이터베이스 기반 쓰기 (format: ‘sql’, ‘database’): -- target_path: 사용되지 않습니다. -- kwargs (dict): 데이터베이스 쓰기에 필요한 추가 인자들입니다.

    -
    -
      -

    • pandas.to_sql: ‘name’(테이블명), ‘con’(커넥션 객체)가 필수입니다.

      • -

    • polars.write_database: ‘table_name’, ‘connection_uri’가 필수입니다.

      • -
    -
    -
    • -
-
-
매개변수:
-
    -

  • obj – 저장할 데이터 객체 (e.g., pandas.DataFrame, polars.DataFrame, np.ndarray).

    • -

  • target_path (str, optional) – 파일 저장 경로. 파일 기반 쓰기 시 필수. Defaults to None.

    • -

  • format (str, optional) – 저장할 포맷. Defaults to None.

    • -

  • show_progress (bool) – 진행도 표시 여부. Defaults to False.

    • -

  • verbose (bool) – 상세한 성능 진단 정보 출력 여부. Defaults to False.

    • -

  • **kwargs – 각 쓰기 함수에 전달될 추가 키워드 인자.

    • -
-
-
-
- -
-
-atio.core.write_snapshot(obj, table_path, mode='overwrite', format='parquet', **kwargs)[소스]
-
- -
-
-atio.core.read_table(table_path, version=None, output_as='pandas')[소스]
-
- -
-
-atio.core.expire_snapshots(table_path, keep_for=datetime.timedelta(days=7), dry_run=True)[소스]
-

설정된 보관 기간(keep_for)보다 오래된 스냅샷과 -더 이상 참조되지 않는 데이터 파일을 삭제합니다.

-
- -
-
-

write()

-

안전한 원자적 파일 쓰기를 수행하는 메인 함수입니다.

-
-
-atio.write(obj, target_path=None, format='parquet', **kwargs)
-
-
매개변수:
-
    -

  • obj – 저장할 데이터 객체 (pandas.DataFrame, polars.DataFrame, numpy.ndarray)

    • -

  • target_path – 저장할 파일 경로 (파일 저장 시 필수)

    • -

  • format – 저장 형식 (‘csv’, ‘parquet’, ‘excel’, ‘json’, ‘sql’, ‘database’)

    • -

  • show_progress – 진행률 표시 여부 (기본값: False)

    • -

  • verbose – 상세 성능 정보 출력 여부 (기본값: False)

    • -

  • **kwargs –

    형식별 추가 매개변수

    -

    • -
-
-
반환:
-

None

-
-
Raises:
-

ValueError, IOError, DatabaseError

-
-
-

사용 예제:

-
import atio
-import pandas as pd
-
-df = pd.DataFrame({"a": [1, 2, 3]})
-
-# 기본 사용법
-atio.write(df, "data.parquet", format="parquet")
-
-# 진행률 표시
-atio.write(df, "data.parquet", format="parquet", show_progress=True)
-
-# 성능 정보 출력
-atio.write(df, "data.parquet", format="parquet", verbose=True)
-
-
-
- -
-
-

write_snapshot()

-

데이터 스냅샷을 생성하여 버전 관리를 수행합니다.

-
-
-atio.write_snapshot(obj, table_name, format='parquet', **kwargs)
-
-
매개변수:
-
    -

  • obj – 저장할 데이터 객체

    • -

  • table_name – 테이블 이름 (스냅샷 디렉토리명)

    • -

  • format – 저장 형식

    • -

  • **kwargs –

    추가 매개변수

    -

    • -
-
-
반환:
-

생성된 스냅샷 ID

-
-
-

사용 예제:

-
# 스냅샷 생성
-snapshot_id = atio.write_snapshot(df, "users", format="parquet")
-print(f"생성된 스냅샷 ID: {snapshot_id}")
-
-
-
- -
-
-

read_table()

-

스냅샷에서 데이터를 읽어옵니다.

-
-
-atio.read_table(table_name, snapshot_id='latest', format='parquet', **kwargs)
-
-
매개변수:
-
    -

  • table_name – 테이블 이름

    • -

  • snapshot_id – 스냅샷 ID (기본값: ‘latest’)

    • -

  • format – 읽을 형식

    • -

  • **kwargs –

    추가 매개변수

    -

    • -
-
-
반환:
-

데이터 객체

-
-
-

사용 예제:

-
# 최신 스냅샷 읽기
-df = atio.read_table("users", format="parquet")
-
-# 특정 스냅샷 읽기
-df = atio.read_table("users", snapshot_id="20240101_120000", format="parquet")
-
-
-
- -
-
-

expire_snapshots()

-

오래된 스냅샷을 정리합니다.

-
-
-atio.expire_snapshots(table_name, days=30, format='parquet')
-
-
매개변수:
-
    -

  • table_name – 테이블 이름

    • -

  • days – 보관할 일수 (기본값: 30)

    • -

  • format – 형식

    • -
-
-
반환:
-

삭제된 스냅샷 수

-
-
-

사용 예제:

-
# 30일 이상 된 스냅샷 정리
-deleted_count = atio.expire_snapshots("users", days=30)
-print(f"삭제된 스냅샷 수: {deleted_count}")
-
-
-
- -
-
-

Plugins 모듈

-
-
-atio.plugins.register_writer(obj_type, fmt, handler)[소스]
-

(객체 타입, 포맷) 쌍으로 쓰기 핸들러를 등록

-
- -
-
-atio.plugins.get_writer(obj, fmt)[소스]
-

객체의 타입과 포맷에 맞는 핸들러를 조회

-
- -
-
-

Utils 모듈

-
-
-atio.utils.setup_logger(name='atio', debug_level=False)[소스]
-
- -
-
-atio.utils.check_file_exists(path)[소스]
-
- -
-
-class atio.utils.ProgressBar(filepath: str, stop_event: Event, description: str = 'Writing')[소스]
-

기반 클래스: object

-

파일 쓰기 진행 상황을 콘솔에 표시하는 클래스. -스피너, 처리된 용량, 처리 속도, 경과 시간을 표시합니다.

-
-
-__init__(filepath: str, stop_event: Event, description: str = 'Writing')[소스]
-
- -
-
-run()[소스]
-

진행도 막대를 실행하는 메인 루프. -이 함수가 모니터링 스레드에서 실행됩니다.

-
- -
- -
-
-atio.utils.read_json(path: str)[소스]
-
- -
-
-atio.utils.write_json(data: dict, path: str)[소스]
-
- -
-
-

지원하는 형식

-
-

CSV 형식

-
atio.write(df, "data.csv", format="csv", index=False, encoding='utf-8')
-
-
-

지원 매개변수: -- index: 인덱스 포함 여부 -- encoding: 인코딩 방식 -- sep: 구분자 -- na_rep: NA 값 표현

-
-
-

Parquet 형식

-
atio.write(df, "data.parquet", format="parquet", compression='snappy')
-
-
-

지원 매개변수: -- compression: 압축 방식 (‘snappy’, ‘gzip’, ‘brotli’) -- engine: 엔진 (‘pyarrow’, ‘fastparquet’)

-
-
-

Excel 형식

-
atio.write(df, "data.xlsx", format="excel", sheet_name="Sheet1")
-
-
-

지원 매개변수: -- sheet_name: 시트 이름 -- engine: 엔진 (‘openpyxl’, ‘xlsxwriter’)

-
-
-

JSON 형식

-
atio.write(df, "data.json", format="json", orient="records")
-
-
-

지원 매개변수: -- orient: 방향 (‘records’, ‘split’, ‘index’, ‘columns’, ‘values’, ‘table’)

-
-
-

SQL 형식

-
atio.write(df, format="sql", name="table_name", con=engine, if_exists="replace")
-
-
-

지원 매개변수: -- name: 테이블 이름 -- con: 데이터베이스 연결 -- if_exists: 테이블 존재 시 동작 (‘fail’, ‘replace’, ‘append’)

-
-
-
-

예외 처리

-

Atio는 다음과 같은 예외를 발생시킬 수 있습니다:

-
    -

  • ValueError: 잘못된 매개변수나 형식

    • -

  • IOError: 파일 시스템 오류

    • -

  • DatabaseError: 데이터베이스 연결 오류

    • -

  • ImportError: 필요한 패키지가 설치되지 않은 경우

    • -
-

예외 처리 예제:

-
try:
-    atio.write(df, "data.parquet", format="parquet")
-except ValueError as e:
-    print(f"매개변수 오류: {e}")
-except IOError as e:
-    print(f"파일 시스템 오류: {e}")
-except Exception as e:
-    print(f"예상치 못한 오류: {e}")
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/configuration.html b/docs/build/html/configuration.html deleted file mode 100644 index 6cb5489..0000000 --- a/docs/build/html/configuration.html +++ /dev/null @@ -1,629 +0,0 @@ - - - - - - - - - 설정 및 구성 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

설정 및 구성

-

Atio의 다양한 설정 옵션과 구성 방법을 설명합니다.

-
-

로깅 설정

-

Atio는 상세한 로깅을 통해 작업 과정을 추적할 수 있습니다.

-
-

기본 로깅

-
import atio
-import pandas as pd
-
-df = pd.DataFrame({"a": [1, 2, 3]})
-
-# 기본 로깅 (INFO 레벨)
-atio.write(df, "data.parquet", format="parquet")
-
-
-

출력 예시: -` -[INFO] 임시 디렉토리 생성: /tmp/tmp12345 -[INFO] 임시 파일 경로: /tmp/tmp12345/data.parquet -[INFO] 사용할 writer: to_parquet (format: parquet) -[INFO] 데이터 임시 파일에 저장 완료: /tmp/tmp12345/data.parquet -[INFO] 원자적 교체 완료: /tmp/tmp12345/data.parquet -> data.parquet -[INFO] _SUCCESS 플래그 파일 생성: .data.parquet._SUCCESS -[INFO] ✅ Atomic write completed successfully (took 0.1234s) -`

-
-
-

상세 로깅

-
# 상세한 성능 정보 출력 (DEBUG 레벨)
-atio.write(df, "data.parquet", format="parquet", verbose=True)
-
-
-

출력 예시: -` -[INFO] 임시 디렉토리 생성: /tmp/tmp12345 -[INFO] 임시 파일 경로: /tmp/tmp12345/data.parquet -[INFO] 사용할 writer: to_parquet (format: parquet) -[INFO] 데이터 임시 파일에 저장 완료: /tmp/tmp12345/data.parquet -[INFO] 원자적 교체 완료: /tmp/tmp12345/data.parquet -> data.parquet -[INFO] _SUCCESS 플래그 파일 생성: .data.parquet._SUCCESS -[DEBUG] Atomic write step timings (SUCCESS): setup=0.0012s, write_call=0.0987s, replace=0.0001s, success_flag=0.0001s, total=0.1001s -[INFO] ✅ Atomic write completed successfully (took 0.1001s) -`

-
-
-
-

진행도 표시

-

대용량 파일 처리 시 진행 상황을 실시간으로 확인할 수 있습니다.

-
-

기본 진행도 표시

-
import atio
-import pandas as pd
-import numpy as np
-
-# 대용량 데이터 생성
-large_df = pd.DataFrame(np.random.randn(1000000, 10))
-
-# 진행도 표시와 함께 저장
-atio.write(large_df, "large_data.parquet",
-           format="parquet",
-           show_progress=True)
-
-
-

출력 예시: -` -⠋ Writing large_data.parquet... [ 45.2 MB | 12.3 MB/s | 00:03 ] -⠙ Writing large_data.parquet... [ 67.8 MB | 11.9 MB/s | 00:05 ] -⠹ Writing large_data.parquet... [ 89.1 MB | 12.1 MB/s | 00:07 ] -✅ Writing completed successfully (89.1 MB in 7s) -`

-
-
-

진행도 표시 옵션

-

진행도 표시는 다음과 같은 정보를 제공합니다:

-
    -

  • 스피너: 작업 진행 상태를 시각적으로 표시

    • -

  • 파일 크기: 현재까지 저장된 데이터 크기

    • -

  • 처리 속도: 초당 처리되는 데이터 양

    • -

  • 경과 시간: 작업 시작 후 경과한 시간

    • -
-
-
-
-

성능 최적화 설정

-
-

메모리 사용량 최적화

-
# 대용량 데이터 처리 시 메모리 효율적인 설정
-atio.write(large_df, "data.parquet", format="parquet",
-           compression='snappy',  # 빠른 압축
-           index=False)          # 인덱스 제외로 메모리 절약
-
-
-
-
-

압축 설정

-
# 속도 우선 (압축 없음)
-atio.write(df, "data.parquet", format="parquet", compression=None)
-
-# 균형 (snappy 압축)
-atio.write(df, "data.parquet", format="parquet", compression='snappy')
-
-# 용량 우선 (gzip 압축)
-atio.write(df, "data.parquet", format="parquet", compression='gzip')
-
-
-
-
-
-

임시 디렉토리 설정

-

기본적으로 Atio는 시스템의 임시 디렉토리를 사용합니다.

-
-

사용자 정의 임시 디렉토리

-
import os
-import tempfile
-
-# 임시 디렉토리 설정
-tempfile.tempdir = "/path/to/custom/temp"
-
-# 또는 환경 변수 설정
-os.environ['TMPDIR'] = "/path/to/custom/temp"
-
-
-

주의사항: -- 임시 디렉토리는 충분한 디스크 공간이 있어야 합니다 -- 쓰기 권한이 있어야 합니다 -- 빠른 I/O 성능을 위해 SSD를 권장합니다

-
-
-
-

에러 처리 설정

-

Atio는 다양한 에러 상황에 대해 안전하게 처리합니다.

-
-

롤백 동작

-
# 기본적으로 롤백이 자동으로 수행됩니다
-try:
-    atio.write(df, "data.parquet", format="parquet")
-except Exception as e:
-    # 에러 발생 시 원본 파일은 보존됩니다
-    print(f"저장 실패: {e}")
-    # 임시 파일은 자동으로 정리됩니다
-
-
-
-
-

백업 파일 관리

-
# 백업 파일은 작업 성공 시 자동으로 삭제됩니다
-# 실패 시에는 롤백 후 삭제됩니다
-
-# 백업 파일이 남아있는 경우 수동으로 확인
-import os
-backup_file = "data.parquet._backup"
-if os.path.exists(backup_file):
-    print("백업 파일이 존재합니다. 수동 확인이 필요할 수 있습니다.")
-
-
-
-
-
-

완료 플래그 시스템

-

Atio는 작업 완료를 확인할 수 있는 플래그 파일을 생성합니다.

-
-

플래그 파일 확인

-
import os
-
-# 저장 완료 후 플래그 파일 확인
-atio.write(df, "data.parquet", format="parquet")
-
-# 플래그 파일 경로
-flag_file = ".data.parquet._SUCCESS"
-
-if os.path.exists(flag_file):
-    print("저장이 성공적으로 완료되었습니다.")
-else:
-    print("저장이 완료되지 않았거나 실패했습니다.")
-
-
-
-
-

플래그 파일 활용

-
# 배치 처리에서 완료 여부 확인
-files_to_process = ["data1.parquet", "data2.parquet", "data3.parquet"]
-
-for file in files_to_process:
-    flag_file = f".{file}._SUCCESS"
-    if not os.path.exists(flag_file):
-        print(f"{file} 처리가 완료되지 않았습니다.")
-        # 재처리 로직
-
-
-
-
-
-

스냅샷 설정

-

스냅샷 시스템의 다양한 설정 옵션을 설명합니다.

-
-

스냅샷 모드

-
# overwrite 모드 (기본값)
-atio.write_snapshot(df, "table_path", mode="overwrite", format="parquet")
-
-# append 모드 (기존 데이터에 추가)
-atio.write_snapshot(df, "table_path", mode="append", format="parquet")
-
-
-
-
-

스냅샷 정리 설정

-
from datetime import timedelta
-
-# 7일 이상 된 스냅샷 삭제
-atio.expire_snapshots("table_path",
-                     keep_for=timedelta(days=7),
-                     dry_run=True)  # 실제 삭제 전 확인
-
-# 30일 이상 된 스냅샷 삭제
-atio.expire_snapshots("table_path",
-                     keep_for=timedelta(days=30),
-                     dry_run=False)  # 실제 삭제
-
-
-
-
-
-

환경 변수 설정

-

Atio의 동작을 제어하는 환경 변수들을 설정할 수 있습니다.

-
-

로깅 레벨 설정

-
import os
-
-# DEBUG 레벨로 로깅 설정
-os.environ['ATIO_LOG_LEVEL'] = 'DEBUG'
-
-# INFO 레벨로 로깅 설정 (기본값)
-os.environ['ATIO_LOG_LEVEL'] = 'INFO'
-
-
-
-
-

임시 디렉토리 설정

-
# 임시 디렉토리 경로 설정
-os.environ['ATIO_TEMP_DIR'] = '/path/to/temp'
-
-# 또는 시스템 임시 디렉토리 설정
-os.environ['TMPDIR'] = '/path/to/temp'
-
-
-
-
-

성능 모니터링 설정

-
# 성능 모니터링 활성화
-os.environ['ATIO_PERFORMANCE_MONITORING'] = 'true'
-
-# 성능 모니터링 비활성화
-os.environ['ATIO_PERFORMANCE_MONITORING'] = 'false'
-
-
-
-
-
-

플러그인 설정

-

커스텀 플러그인을 등록하여 새로운 형식을 지원할 수 있습니다.

-
-

플러그인 등록

-
from atio.plugins import register_writer
-import pandas as pd
-
-# 커스텀 형식 등록
-def custom_writer(df, path, **kwargs):
-    # 커스텀 저장 로직
-    with open(path, 'w') as f:
-        f.write("Custom format\n")
-        f.write(df.to_string())
-
-# 등록
-register_writer(pd.DataFrame, "custom", custom_writer)
-
-
-
-
-

플러그인 확인

-
from atio.plugins import WRITER_MAPPING
-
-# 등록된 플러그인 확인
-for obj_type, formats in WRITER_MAPPING.items():
-    print(f"Object type: {obj_type.__name__}")
-    for fmt, handler in formats.items():
-        print(f"  - {fmt}: {handler}")
-
-
-
-
-
-

설정 파일 사용

-

설정을 파일로 관리하여 일관된 설정을 유지할 수 있습니다.

-
-

JSON 설정 파일

-
import json
-
-# 설정 파일 생성
-config = {
-    "default_format": "parquet",
-    "compression": "snappy",
-    "show_progress": True,
-    "verbose": False,
-    "temp_dir": "/path/to/temp"
-}
-
-with open("atio_config.json", "w") as f:
-    json.dump(config, f, indent=2)
-
-# 설정 파일 읽기
-with open("atio_config.json", "r") as f:
-    config = json.load(f)
-
-# 설정 적용
-atio.write(df, "data.parquet",
-           format=config.get("default_format", "parquet"),
-           compression=config.get("compression", "snappy"),
-           show_progress=config.get("show_progress", False),
-           verbose=config.get("verbose", False))
-
-
-
-
-

YAML 설정 파일

-
import yaml
-
-# 설정 파일 생성
-config = {
-    "default_format": "parquet",
-    "compression": "snappy",
-    "show_progress": True,
-    "verbose": False,
-    "temp_dir": "/path/to/temp"
-}
-
-with open("atio_config.yaml", "w") as f:
-    yaml.dump(config, f)
-
-# 설정 파일 읽기
-with open("atio_config.yaml", "r") as f:
-    config = yaml.safe_load(f)
-
-
-
-
-
-

모범 사례

-
-

프로덕션 환경 설정

-
# 프로덕션 환경을 위한 설정
-import os
-import tempfile
-
-# 1. 전용 임시 디렉토리 설정
-tempfile.tempdir = "/var/tmp/atio"
-os.makedirs(tempfile.tempdir, exist_ok=True)
-
-# 2. 로깅 레벨 설정
-os.environ['ATIO_LOG_LEVEL'] = 'INFO'
-
-# 3. 성능 최적화 설정
-def safe_write(df, path, **kwargs):
-    return atio.write(df, path,
-                     format="parquet",
-                     compression="snappy",
-                     show_progress=True,
-                     verbose=False,
-                     **kwargs)
-
-
-
-
-

개발 환경 설정

-
# 개발 환경을 위한 설정
-import os
-
-# 1. 상세 로깅 활성화
-os.environ['ATIO_LOG_LEVEL'] = 'DEBUG'
-
-# 2. 성능 모니터링 활성화
-os.environ['ATIO_PERFORMANCE_MONITORING'] = 'true'
-
-# 3. 개발용 설정
-def dev_write(df, path, **kwargs):
-    return atio.write(df, path,
-                     format="parquet",
-                     compression=None,  # 압축 없음으로 빠른 처리
-                     show_progress=True,
-                     verbose=True,  # 상세 정보 출력
-                     **kwargs)
-
-
-
-
-
-

설정 검증

-

설정이 올바르게 적용되었는지 확인하는 방법을 설명합니다.

-
-

기본 검증

-
import atio
-import pandas as pd
-import tempfile
-
-# 테스트 데이터 생성
-df = pd.DataFrame({"test": [1, 2, 3]})
-
-# 설정 테스트
-def test_config():
-    # 임시 디렉토리 확인
-    print(f"임시 디렉토리: {tempfile.gettempdir()}")
-
-    # 로깅 테스트
-    atio.write(df, "test.parquet", format="parquet", verbose=True)
-
-    # 플래그 파일 확인
-    import os
-    if os.path.exists(".test.parquet._SUCCESS"):
-        print("설정이 올바르게 작동합니다.")
-    else:
-        print("설정에 문제가 있을 수 있습니다.")
-
-
-
-
-

성능 테스트

-
import time
-import pandas as pd
-import numpy as np
-
-# 성능 테스트
-def performance_test():
-    # 대용량 데이터 생성
-    large_df = pd.DataFrame(np.random.randn(100000, 10))
-
-    # 성능 측정
-    start_time = time.time()
-    atio.write(large_df, "performance_test.parquet",
-               format="parquet",
-               show_progress=True)
-    end_time = time.time()
-
-    print(f"처리 시간: {end_time - start_time:.2f}초")
-
-    # 파일 크기 확인
-    import os
-    file_size = os.path.getsize("performance_test.parquet")
-    print(f"파일 크기: {file_size / 1024 / 1024:.2f} MB")
-
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/examples.html b/docs/build/html/examples.html deleted file mode 100644 index 75ea738..0000000 --- a/docs/build/html/examples.html +++ /dev/null @@ -1,539 +0,0 @@ - - - - - - - - - 사용 예제 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

사용 예제

-

Atio의 다양한 사용 사례와 고급 기능을 살펴보세요.

-
-

기본 예제

-
-

간단한 데이터 저장

-
import atio
-import pandas as pd
-
-# 샘플 데이터 생성
-data = {
-    "id": [1, 2, 3, 4, 5],
-    "name": ["Alice", "Bob", "Charlie", "Diana", "Eve"],
-    "age": [25, 30, 35, 28, 32],
-    "city": ["Seoul", "Busan", "Incheon", "Daegu", "Daejeon"],
-    "salary": [50000, 60000, 70000, 55000, 65000]
-}
-
-df = pd.DataFrame(data)
-
-# 다양한 형식으로 저장
-atio.write(df, "employees.parquet", format="parquet")
-atio.write(df, "employees.csv", format="csv", index=False)
-atio.write(df, "employees.xlsx", format="excel", sheet_name="Employees")
-
-
-
-
-
-

대용량 데이터 처리

-
-

진행률 표시와 성능 모니터링

-
import numpy as np
-import pandas as pd
-import atio
-
-# 대용량 데이터 생성 (100만 행)
-large_data = {
-    "id": range(1, 1000001),
-    "value": np.random.randn(1000000),
-    "category": np.random.choice(["A", "B", "C"], 1000000),
-    "timestamp": pd.date_range("2024-01-01", periods=1000000, freq="S")
-}
-
-large_df = pd.DataFrame(large_data)
-
-# 진행률 표시와 성능 모니터링 활성화
-atio.write(
-    large_df,
-    "large_dataset.parquet",
-    format="parquet",
-    show_progress=True,
-    verbose=True,
-    compression='snappy'
-)
-
-
-
-
-
-

데이터베이스 연동

-
-

PostgreSQL 데이터베이스 저장

-
import atio
-import pandas as pd
-from sqlalchemy import create_engine
-
-# 데이터베이스 연결
-engine = create_engine('postgresql://username:password@localhost:5432/mydb')
-
-# 샘플 데이터
-sales_data = {
-    "order_id": [1001, 1002, 1003, 1004, 1005],
-    "product_name": ["Laptop", "Mouse", "Keyboard", "Monitor", "Headphones"],
-    "quantity": [1, 2, 1, 1, 3],
-    "price": [1200, 25, 75, 300, 150],
-    "order_date": pd.date_range("2024-01-01", periods=5)
-}
-
-sales_df = pd.DataFrame(sales_data)
-
-# 데이터베이스에 안전하게 저장
-atio.write(
-    sales_df,
-    format="sql",
-    name="sales_orders",
-    con=engine,
-    if_exists="replace",
-    index=False
-)
-
-
-
-
-
-

스냅샷 기반 버전 관리

-
-

데이터 버전 관리

-
import atio
-import pandas as pd
-from datetime import datetime, timedelta
-
-# 초기 데이터
-initial_data = {
-    "user_id": [1, 2, 3],
-    "name": ["Alice", "Bob", "Charlie"],
-    "status": ["active", "active", "inactive"]
-}
-
-df = pd.DataFrame(initial_data)
-
-# 초기 스냅샷 생성
-snapshot_id_1 = atio.write_snapshot(df, "users", format="parquet")
-print(f"초기 스냅샷 생성: {snapshot_id_1}")
-
-# 데이터 업데이트
-df.loc[df['user_id'] == 3, 'status'] = 'active'
-df = df.append({"user_id": 4, "name": "Diana", "status": "active"}, ignore_index=True)
-
-# 업데이트된 스냅샷 생성
-snapshot_id_2 = atio.write_snapshot(df, "users", format="parquet")
-print(f"업데이트 스냅샷 생성: {snapshot_id_2}")
-
-# 최신 데이터 읽기
-latest_df = atio.read_table("users", format="parquet")
-print("최신 데이터:")
-print(latest_df)
-
-# 특정 스냅샷 읽기
-initial_df = atio.read_table("users", snapshot_id=snapshot_id_1, format="parquet")
-print("초기 데이터:")
-print(initial_df)
-
-# 오래된 스냅샷 정리 (7일 이상)
-deleted_count = atio.expire_snapshots("users", days=7, format="parquet")
-print(f"삭제된 스냅샷 수: {deleted_count}")
-
-
-
-
-
-

Polars DataFrame 활용

-
-

고성능 데이터 처리

-
import atio
-import polars as pl
-import numpy as np
-
-# Polars DataFrame 생성
-polars_data = {
-    "id": range(1, 10001),
-    "value": np.random.randn(10000),
-    "category": np.random.choice(["A", "B", "C", "D"], 10000),
-    "score": np.random.uniform(0, 100, 10000)
-}
-
-polars_df = pl.DataFrame(polars_data)
-
-# Polars DataFrame 저장
-atio.write(
-    polars_df,
-    "polars_data.parquet",
-    format="parquet",
-    compression='snappy',
-    show_progress=True
-)
-
-# 데이터 변환 후 저장
-filtered_df = polars_df.filter(pl.col("score") > 50)
-aggregated_df = filtered_df.group_by("category").agg(
-    pl.col("value").mean().alias("avg_value"),
-    pl.col("score").mean().alias("avg_score")
-)
-
-atio.write(aggregated_df, "aggregated_data.parquet", format="parquet")
-
-
-
-
-
-

에러 처리 및 복구

-
-

안전한 데이터 처리

-
import atio
-import pandas as pd
-import os
-
-def safe_data_processing():
-    try:
-        # 원본 파일이 있는지 확인
-        if os.path.exists("important_data.parquet"):
-            print("원본 파일이 존재합니다.")
-
-        # 데이터 처리 및 저장
-        df = pd.DataFrame({
-            "id": [1, 2, 3],
-            "data": ["important", "data", "here"]
-        })
-
-        atio.write(df, "important_data.parquet", format="parquet")
-        print("데이터가 안전하게 저장되었습니다.")
-
-        # SUCCESS 파일 확인
-        if os.path.exists("important_data.parquet_SUCCESS"):
-            print("저장 완료 플래그가 생성되었습니다.")
-
-    except Exception as e:
-        print(f"오류 발생: {e}")
-        print("원본 파일이 보존되었습니다.")
-
-        # 임시 파일 정리
-        temp_files = [f for f in os.listdir(".") if f.startswith("important_data.parquet.tmp")]
-        for temp_file in temp_files:
-            try:
-                os.remove(temp_file)
-                print(f"임시 파일 정리: {temp_file}")
-            except:
-                pass
-
-# 안전한 데이터 처리 실행
-safe_data_processing()
-
-
-
-
-
-

배치 처리

-
-

여러 파일 동시 처리

-
import atio
-import pandas as pd
-import os
-from pathlib import Path
-
-def process_multiple_files():
-    # 처리할 파일 목록
-    files_to_process = [
-        {"name": "users", "data": pd.DataFrame({"id": [1, 2], "name": ["Alice", "Bob"]})},
-        {"name": "products", "data": pd.DataFrame({"id": [1, 2], "product": ["Laptop", "Mouse"]})},
-        {"name": "orders", "data": pd.DataFrame({"id": [1, 2], "amount": [100, 200]})}
-    ]
-
-    # 각 파일을 안전하게 처리
-    for file_info in files_to_process:
-        try:
-            file_path = f"{file_info['name']}.parquet"
-            atio.write(
-                file_info['data'],
-                file_path,
-                format="parquet",
-                show_progress=True
-            )
-            print(f"{file_info['name']} 파일 처리 완료")
-
-        except Exception as e:
-            print(f"{file_info['name']} 파일 처리 실패: {e}")
-            continue
-
-# 배치 처리 실행
-process_multiple_files()
-
-
-
-
-
-

성능 최적화

-
-

압축 및 최적화 설정

-
import atio
-import pandas as pd
-import numpy as np
-
-# 대용량 데이터 생성
-large_df = pd.DataFrame({
-    "id": range(1, 100001),
-    "value": np.random.randn(100000),
-    "text": ["sample text"] * 100000
-})
-
-# 다양한 압축 설정으로 성능 비교
-compression_settings = [
-    ("snappy", "fast_compression.parquet"),
-    ("gzip", "balanced_compression.parquet"),
-    ("brotli", "high_compression.parquet")
-]
-
-for compression, filename in compression_settings:
-    print(f"\n{compression} 압축으로 저장 중...")
-    atio.write(
-        large_df,
-        filename,
-        format="parquet",
-        compression=compression,
-        show_progress=True,
-        verbose=True
-    )
-
-    # 파일 크기 확인
-    file_size = os.path.getsize(filename) / (1024 * 1024)  # MB
-    print(f"파일 크기: {file_size:.2f} MB")
-
-
-
-
-
-

실제 사용 사례

-
-

데이터 파이프라인

-
import atio
-import pandas as pd
-from datetime import datetime, timedelta
-
-class DataPipeline:
-    def __init__(self, base_path="data"):
-        self.base_path = Path(base_path)
-        self.base_path.mkdir(exist_ok=True)
-
-    def extract_data(self):
-        """데이터 추출 (시뮬레이션)"""
-        # 실제로는 API나 데이터베이스에서 데이터를 가져옴
-        data = {
-            "timestamp": pd.date_range(datetime.now(), periods=1000, freq="H"),
-            "value": np.random.randn(1000),
-            "source": ["api"] * 1000
-        }
-        return pd.DataFrame(data)
-
-    def transform_data(self, df):
-        """데이터 변환"""
-        # 시간별 집계
-        df['hour'] = df['timestamp'].dt.hour
-        df['day'] = df['timestamp'].dt.date
-
-        # 일별 통계
-        daily_stats = df.groupby('day').agg({
-            'value': ['mean', 'std', 'min', 'max']
-        }).round(2)
-
-        return daily_stats
-
-    def load_data(self, df, table_name):
-        """데이터 로드"""
-        # 스냅샷 생성
-        snapshot_id = atio.write_snapshot(
-            df,
-            table_name,
-            format="parquet"
-        )
-
-        # 최신 데이터도 별도 저장
-        latest_path = self.base_path / f"{table_name}_latest.parquet"
-        atio.write(df, str(latest_path), format="parquet")
-
-        return snapshot_id
-
-    def run_pipeline(self):
-        """파이프라인 실행"""
-        print("데이터 파이프라인 시작...")
-
-        # 1. 데이터 추출
-        raw_data = self.extract_data()
-        print(f"추출된 데이터: {len(raw_data)} 행")
-
-        # 2. 데이터 변환
-        processed_data = self.transform_data(raw_data)
-        print(f"처리된 데이터: {len(processed_data)} 행")
-
-        # 3. 데이터 로드
-        snapshot_id = self.load_data(processed_data, "daily_stats")
-        print(f"스냅샷 생성 완료: {snapshot_id}")
-
-        # 4. 오래된 스냅샷 정리
-        deleted_count = atio.expire_snapshots("daily_stats", days=30)
-        print(f"정리된 스냅샷: {deleted_count}개")
-
-        print("파이프라인 완료!")
-
-# 파이프라인 실행
-pipeline = DataPipeline()
-pipeline.run_pipeline()
-
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/formats.html b/docs/build/html/formats.html deleted file mode 100644 index 7d12e18..0000000 --- a/docs/build/html/formats.html +++ /dev/null @@ -1,516 +0,0 @@ - - - - - - - - - 지원하는 파일 형식 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

지원하는 파일 형식

-

Atio는 다양한 데이터 형식을 지원합니다. 각 형식별로 지원하는 라이브러리와 사용법을 설명합니다.

-
-

Pandas DataFrame 형식

-

Pandas DataFrame은 가장 많은 형식을 지원합니다.

-
-

CSV (Comma-Separated Values)

-
import atio
-import pandas as pd
-
-df = pd.DataFrame({
-    "name": ["Alice", "Bob", "Charlie"],
-    "age": [25, 30, 35],
-    "city": ["Seoul", "Busan", "Incheon"]
-})
-
-# 기본 CSV 저장
-atio.write(df, "users.csv", format="csv")
-
-# 추가 옵션과 함께 저장
-atio.write(df, "users.csv", format="csv",
-           index=False,
-           encoding='utf-8',
-           sep=';')
-
-
-

지원 옵션: -- index: 인덱스 포함 여부 (기본값: True) -- encoding: 인코딩 방식 (기본값: ‘utf-8’) -- sep: 구분자 (기본값: ‘,’) -- header: 헤더 포함 여부 (기본값: True)

-
-
-

Parquet

-
# Parquet 형식으로 저장
-atio.write(df, "users.parquet", format="parquet")
-
-# 압축 옵션과 함께 저장
-atio.write(df, "users.parquet", format="parquet",
-           compression='snappy')
-
-
-

지원 옵션: -- compression: 압축 방식 (‘snappy’, ‘gzip’, ‘brotli’, None) -- index: 인덱스 포함 여부 (기본값: True)

-
-
-

Excel

-
# Excel 파일로 저장
-atio.write(df, "users.xlsx", format="excel")
-
-# 시트명과 옵션 지정
-atio.write(df, "users.xlsx", format="excel",
-           sheet_name="Users",
-           index=False)
-
-
-

지원 옵션: -- sheet_name: 시트 이름 (기본값: ‘Sheet1’) -- index: 인덱스 포함 여부 (기본값: True) -- engine: 엔진 (‘openpyxl’, ‘xlsxwriter’)

-

필요 라이브러리: pip install openpyxl

-
-
-

JSON

-
# JSON 형식으로 저장
-atio.write(df, "users.json", format="json")
-
-# 들여쓰기와 함께 저장
-atio.write(df, "users.json", format="json",
-           indent=2,
-           orient='records')
-
-
-

지원 옵션: -- orient: JSON 구조 (‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’) -- indent: 들여쓰기 크기 -- date_format: 날짜 형식

-
-
-

Pickle

-
# Pickle 형식으로 저장
-atio.write(df, "users.pkl", format="pickle")
-
-# 압축과 함께 저장
-atio.write(df, "users.pkl", format="pickle",
-           compression='gzip')
-
-
-

지원 옵션: -- compression: 압축 방식 (‘gzip’, ‘bz2’, ‘xz’, None)

-
-
-

HTML

-
# HTML 테이블로 저장
-atio.write(df, "users.html", format="html")
-
-# 스타일과 함께 저장
-atio.write(df, "users.html", format="html",
-           index=False,
-           classes='table table-striped')
-
-
-

지원 옵션: -- classes: CSS 클래스 -- index: 인덱스 포함 여부 (기본값: True)

-
-
-

SQL

-
import atio
-import pandas as pd
-from sqlalchemy import create_engine
-
-# 데이터베이스 연결
-engine = create_engine('postgresql://user:password@localhost/dbname')
-
-df = pd.DataFrame({
-    "id": [1, 2, 3],
-    "name": ["Alice", "Bob", "Charlie"]
-})
-
-# SQL 데이터베이스에 저장
-atio.write(df, format="sql",
-           name="users",
-           con=engine,
-           if_exists='replace')
-
-
-

지원 옵션: -- name: 테이블 이름 (필수) -- con: 데이터베이스 연결 객체 (필수) -- if_exists: 테이블이 존재할 때 동작 (‘fail’, ‘replace’, ‘append’) -- index: 인덱스를 컬럼으로 저장 여부 (기본값: True)

-

필요 라이브러리: pip install sqlalchemy

-
-
-
-

Polars DataFrame 형식

-

Polars는 빠른 데이터 처리를 위한 현대적인 DataFrame 라이브러리입니다.

-
-

CSV

-
import atio
-import polars as pl
-
-df = pl.DataFrame({
-    "name": ["Alice", "Bob", "Charlie"],
-    "age": [25, 30, 35],
-    "city": ["Seoul", "Busan", "Incheon"]
-})
-
-# CSV 저장
-atio.write(df, "users.csv", format="csv")
-
-# 구분자와 함께 저장
-atio.write(df, "users.csv", format="csv",
-           separator=';')
-
-
-

지원 옵션: -- separator: 구분자 (기본값: ‘,’) -- include_header: 헤더 포함 여부 (기본값: True)

-
-
-

Parquet

-
# Parquet 저장
-atio.write(df, "users.parquet", format="parquet")
-
-# 압축과 함께 저장
-atio.write(df, "users.parquet", format="parquet",
-           compression="snappy")
-
-
-

지원 옵션: -- compression: 압축 방식 (‘snappy’, ‘gzip’, ‘brotli’, ‘lz4raw’, ‘zstd’, None)

-
-
-

JSON

-
# JSON 저장
-atio.write(df, "users.json", format="json")
-
-# 파일별 저장
-atio.write(df, "users.json", format="json",
-           file=True)
-
-
-

지원 옵션: -- file: 파일별 저장 여부 (기본값: False)

-
-
-

IPC (Arrow)

-
# IPC (Arrow) 형식으로 저장
-atio.write(df, "users.arrow", format="ipc")
-
-# 압축과 함께 저장
-atio.write(df, "users.arrow", format="ipc",
-           compression="lz4")
-
-
-

지원 옵션: -- compression: 압축 방식 (‘lz4’, ‘zstd’, None)

-
-
-

Avro

-
# Avro 형식으로 저장
-atio.write(df, "users.avro", format="avro")
-
-
-

필요 라이브러리: pip install fastavro

-
-
-

Excel

-
# Excel 저장
-atio.write(df, "users.xlsx", format="excel")
-
-
-

필요 라이브러리: pip install xlsx2csv openpyxl

-
-
-

Database

-
# 데이터베이스에 저장
-atio.write(df, format="database",
-           table_name="users",
-           connection_uri="postgresql://user:password@localhost/dbname")
-
-
-

지원 옵션: -- table_name: 테이블 이름 (필수) -- connection_uri: 데이터베이스 연결 URI (필수)

-

필요 라이브러리: pip install connectorx

-
-
-
-

NumPy 배열 형식

-

NumPy 배열은 수치 데이터 처리에 최적화된 형식을 지원합니다.

-
-

NPY (NumPy Binary)

-
import atio
-import numpy as np
-
-arr = np.array([[1, 2, 3], [4, 5, 6]])
-
-# .npy 파일로 저장
-atio.write(arr, "array.npy", format="npy")
-
-
-

특징: -- 단일 배열을 효율적으로 저장 -- 메타데이터와 함께 저장 -- 빠른 읽기/쓰기 속도

-
-
-

NPZ (NumPy Compressed)

-
# 여러 배열을 딕셔너리로 저장
-arrays = {
-    "features": np.random.randn(1000, 10),
-    "labels": np.random.randint(0, 2, 1000),
-    "metadata": np.array([1, 2, 3, 4, 5])
-}
-
-# 압축되지 않은 .npz 파일로 저장
-atio.write(arrays, "data.npz", format="npz")
-
-# 압축된 .npz 파일로 저장
-atio.write(arrays, "data.npz", format="npz_compressed")
-
-
-

특징: -- 여러 배열을 하나의 파일에 저장 -- 압축 옵션으로 저장 공간 절약 -- 딕셔너리 형태로 데이터 구조화

-
-
-

CSV

-
# CSV로 저장
-atio.write(arr, "array.csv", format="csv")
-
-# 구분자와 함께 저장
-atio.write(arr, "array.csv", format="csv",
-           delimiter=';',
-           fmt='%.2f')
-
-
-

지원 옵션: -- delimiter: 구분자 (기본값: ‘,’) -- fmt: 숫자 형식 (예: ‘%.2f’, ‘%.4e’) -- header: 헤더 포함 여부 -- comments: 주석 문자

-
-
-
-

형식별 성능 비교

-

다양한 형식의 성능 특성을 비교해보겠습니다.

-
-

속도 비교

-
    -

  1. 가장 빠른 형식: -- NumPy: .npy, .npz -- Polars: .ipc (Arrow) -- Pandas: .parquet (snappy 압축)

    2. -

  2. 중간 속도: -- CSV (단순한 구조) -- JSON (중간 복잡도)

    3. -

  3. 상대적으로 느린 형식: -- Excel (복잡한 구조) -- Pickle (Python 특화)

    4. -
-
-
-

용량 비교

-
    -

  1. 가장 작은 용량: -- .parquet (컬럼 기반 압축) -- .npz_compressed (압축된 NumPy) -- .ipc (Arrow 압축)

    2. -

  2. 중간 용량: -- .npy (단일 배열) -- JSON (텍스트 기반)

    3. -

  3. 상대적으로 큰 용량: -- CSV (텍스트 기반) -- Excel (복잡한 구조)

    4. -
-
-
-

호환성 비교

-
    -

  1. 최고 호환성: -- CSV (모든 시스템에서 지원) -- JSON (웹 표준)

    2. -

  2. 좋은 호환성: -- Excel (비즈니스 환경) -- Parquet (빅데이터 생태계)

    3. -

  3. 제한적 호환성: -- .npy/.npz (Python/NumPy 특화) -- .ipc (Arrow 생태계)

    4. -
-
-
-
-

권장 사용 사례

-
-

데이터 분석 및 머신러닝

-
# 학습 데이터: 빠른 읽기/쓰기를 위해 Parquet 사용
-atio.write(training_data, "train.parquet", format="parquet")
-
-# 모델 가중치: NumPy 배열로 저장
-atio.write(model_weights, "weights.npy", format="npy")
-
-# 실험 결과: JSON으로 저장 (가독성)
-atio.write(results, "experiment_results.json", format="json")
-
-
-
-
-

웹 애플리케이션

-
# API 응답: JSON 형식
-atio.write(api_data, "response.json", format="json")
-
-# 대용량 데이터: Parquet 형식
-atio.write(large_dataset, "dataset.parquet", format="parquet")
-
-
-
-
-

데이터 파이프라인

-
# 중간 결과: 빠른 처리를 위해 IPC 사용
-atio.write(intermediate_data, "step1.arrow", format="ipc")
-
-# 최종 결과: 호환성을 위해 CSV 사용
-atio.write(final_data, "output.csv", format="csv")
-
-
-
-
-

비즈니스 보고서

-
# 엑셀 보고서
-atio.write(report_data, "monthly_report.xlsx", format="excel")
-
-# HTML 대시보드
-atio.write(dashboard_data, "dashboard.html", format="html")
-
-
-
-
-
-

형식 확장하기

-

새로운 형식을 추가하려면 플러그인 시스템을 사용하세요.

-
from atio.plugins import register_writer
-import pandas as pd
-
-# 커스텀 형식 등록
-def yaml_writer(df, path, **kwargs):
-    import yaml
-    data = df.to_dict('records')
-    with open(path, 'w') as f:
-        yaml.dump(data, f, **kwargs)
-
-# 등록
-register_writer(pd.DataFrame, "yaml", yaml_writer)
-
-# 사용
-atio.write(df, "data.yaml", format="yaml")
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html deleted file mode 100644 index 5d6b9d5..0000000 --- a/docs/build/html/genindex.html +++ /dev/null @@ -1,265 +0,0 @@ - - - - - - - - 색인 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
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    -
    • -
  • 색인
    • -
  • -
    • -
-
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- - -

색인

- -
- _ - | A - | C - | E - | G - | M - | P - | R - | S - | W - -
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_

- - -
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A

- - - -
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C

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G

- - -
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M

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W

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- - - - \ No newline at end of file diff --git a/docs/build/html/index.html b/docs/build/html/index.html deleted file mode 100644 index fd3d738..0000000 --- a/docs/build/html/index.html +++ /dev/null @@ -1,440 +0,0 @@ - - - - - - - - - Atio Documentation — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
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-
- -
-

Atio Documentation

-

Atio 🛡️는 안전하고 원자적인 파일 쓰기를 지원하는 경량 Python 라이브러리입니다.

-

Pandas, Polars, NumPy 등 데이터 객체 저장 시 파일 손상 없이, **트랜잭션처럼 안전하게 처리**할 수 있습니다.

-
-

주요 기능

-
-
🔒 Atomic File Writing

Safe writing using temporary files

-
-
📊 Multiple Format Support

CSV, Parquet, Excel, JSON, etc.

-
-
🗄️ Database Support

Direct SQL and Database writing

-
-
📈 Progress Display

Progress monitoring for large data processing

-
-
🔄 Rollback Function

Automatic recovery when errors occur

-
-
🎯 Simple API

Intuitive and easy-to-use interface

-
-
📋 Version Management

Snapshot-based data version management

-
-
🧹 Auto Cleanup

Automatic deletion of old data

-
-
-
-
-

빠른 시작

-
import atio
-import pandas as pd
-
-# 간단한 DataFrame 생성
-df = pd.DataFrame({
-    "name": ["Alice", "Bob", "Charlie"],
-    "age": [25, 30, 35],
-    "city": ["Seoul", "Busan", "Incheon"]
-})
-
-# 안전한 파일 쓰기
-atio.write(df, "users.parquet", format="parquet")
-
-# 진행도 표시와 함께 저장
-atio.write(df, "users.csv", format="csv", show_progress=True)
-
-
-
-
-

지원하는 형식

- - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
지원하는 파일 형식

형식

Pandas

Polars

NumPy

CSV

✅

✅

✅

Parquet

✅

✅

❌

Excel

✅

✅

❌

JSON

✅

✅

❌

Pickle

✅

❌

❌

HTML

✅

❌

❌

SQL

✅

❌

❌

Database

❌

✅

❌

NPY/NPZ

❌

❌

✅

-
-
-

사용 사례

-
-
🔹 데이터 파이프라인

ETL 과정에서 중간 데이터 안전하게 저장

-
-
🔹 실험 데이터 관리

머신러닝 실험 결과의 버전 관리

-
-
🔹 대용량 데이터 처리

대용량 파일의 안전한 저장 및 진행도 모니터링

-
-
🔹 데이터베이스 연동

Pandas/Polars 데이터를 SQL/NoSQL DB에 안전하게 저장

-
-
-
-
-

목차

-
-

사용자 가이드:

- -
-
-

API 참조:

- -
-
-
-
-

API Reference

-
-

핵심 함수들

-

Atio: 안전한 원자적 파일 쓰기 라이브러리

-

progress 적용 후 write 함수

-
-
-atio.core.write(obj, target_path=None, format=None, show_progress=False, verbose=False, **kwargs)[소스]
-

데이터 객체(obj)를 안전하게 target_path 또는 데이터베이스에 저장합니다.

-
    -

  • 파일 기반 쓰기 (format: ‘csv’, ‘parquet’, ‘excel’ 등): -- target_path (str): 필수. 데이터가 저장될 파일 경로입니다. -- 롤백 기능이 있는 원자적 쓰기를 수행합니다.

    • -

  • 데이터베이스 기반 쓰기 (format: ‘sql’, ‘database’): -- target_path: 사용되지 않습니다. -- kwargs (dict): 데이터베이스 쓰기에 필요한 추가 인자들입니다.

    -
    -
      -

    • pandas.to_sql: ‘name’(테이블명), ‘con’(커넥션 객체)가 필수입니다.

      • -

    • polars.write_database: ‘table_name’, ‘connection_uri’가 필수입니다.

      • -
    -
    -
    • -
-
-
매개변수:
-
    -

  • obj – 저장할 데이터 객체 (e.g., pandas.DataFrame, polars.DataFrame, np.ndarray).

    • -

  • target_path (str, optional) – 파일 저장 경로. 파일 기반 쓰기 시 필수. Defaults to None.

    • -

  • format (str, optional) – 저장할 포맷. Defaults to None.

    • -

  • show_progress (bool) – 진행도 표시 여부. Defaults to False.

    • -

  • verbose (bool) – 상세한 성능 진단 정보 출력 여부. Defaults to False.

    • -

  • **kwargs – 각 쓰기 함수에 전달될 추가 키워드 인자.

    • -
-
-
-
- -
-
-atio.core.write_snapshot(obj, table_path, mode='overwrite', format='parquet', **kwargs)[소스]
-
- -
-
-atio.core.read_table(table_path, version=None, output_as='pandas')[소스]
-
- -
-
-atio.core.expire_snapshots(table_path, keep_for=datetime.timedelta(days=7), dry_run=True)[소스]
-

설정된 보관 기간(keep_for)보다 오래된 스냅샷과 -더 이상 참조되지 않는 데이터 파일을 삭제합니다.

-
- -
-
-atio.plugins.register_writer(obj_type, fmt, handler)[소스]
-

(객체 타입, 포맷) 쌍으로 쓰기 핸들러를 등록

-
- -
-
-atio.plugins.get_writer(obj, fmt)[소스]
-

객체의 타입과 포맷에 맞는 핸들러를 조회

-
- -
-
-atio.utils.setup_logger(name='atio', debug_level=False)[소스]
-
- -
-
-atio.utils.check_file_exists(path)[소스]
-
- -
-
-class atio.utils.ProgressBar(filepath: str, stop_event: Event, description: str = 'Writing')[소스]
-

파일 쓰기 진행 상황을 콘솔에 표시하는 클래스. -스피너, 처리된 용량, 처리 속도, 경과 시간을 표시합니다.

-
-
-__init__(filepath: str, stop_event: Event, description: str = 'Writing')[소스]
-
- -
-
-run()[소스]
-

진행도 막대를 실행하는 메인 루프. -이 함수가 모니터링 스레드에서 실행됩니다.

-
- -
- -
-
-atio.utils.read_json(path: str)[소스]
-
- -
-
-atio.utils.write_json(data: dict, path: str)[소스]
-
- -
-
-
-

Indices and tables

- -
- - -
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- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/installation.html b/docs/build/html/installation.html deleted file mode 100644 index 6e08980..0000000 --- a/docs/build/html/installation.html +++ /dev/null @@ -1,310 +0,0 @@ - - - - - - - - - 설치 가이드 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - - -
- - -
- -
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- -
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설치 가이드

-

Atio를 설치하는 방법을 안내합니다.

-
-

PyPI를 통한 설치

-

가장 간단한 설치 방법은 PyPI를 사용하는 것입니다:

-
pip install atio
-
-
-
-

특정 버전 설치

-

특정 버전을 설치하려면:

-
pip install atio==2.0.0
-
-
-
-
-

개발 버전 설치

-

최신 개발 버전을 설치하려면:

-
pip install git+https://github.com/seojaeohcode/atio.git
-
-
-
-
-
-

의존성

-
-

필수 의존성

-
    -

  • Python 3.8+

    • -

  • pandas

    • -

  • numpy

    • -
-
-
-

선택적 의존성

-

특정 형식을 사용하려면 추가 라이브러리가 필요합니다:

-

Parquet 형식: -.. code-block:: bash

-
-

pip install pyarrow -# 또는 -pip install fastparquet

-
-

Excel 형식: -.. code-block:: bash

-
-

pip install openpyxl -# 또는 -pip install xlsxwriter

-
-

SQL 데이터베이스: -.. code-block:: bash

-
-

pip install sqlalchemy

-
-

Polars 지원: -.. code-block:: bash

-
-

pip install polars

-
-

Avro 형식 (Polars): -.. code-block:: bash

-
-

pip install fastavro

-
-

Polars Excel 지원: -.. code-block:: bash

-
-

pip install xlsx2csv openpyxl

-
-

Polars 데이터베이스 지원: -.. code-block:: bash

-
-

pip install connectorx

-
-
-
-

모든 의존성 설치

-

모든 기능을 사용하려면:

-
pip install atio[all]
-
-
-

또는 개별적으로:

-
pip install atio
-pip install pyarrow openpyxl sqlalchemy polars fastavro xlsx2csv connectorx
-
-
-
-
-
-

환경 확인

-

설치가 완료되었는지 확인하려면:

-
import atio
-print(f"Atio 버전: {atio.__version__}")
-
-# 기본 기능 테스트
-import pandas as pd
-df = pd.DataFrame({"test": [1, 2, 3]})
-atio.write(df, "test.parquet", format="parquet")
-print("설치가 성공적으로 완료되었습니다!")
-
-
-
-
-

가상환경 사용 권장

-

프로젝트별로 독립적인 환경을 유지하기 위해 가상환경 사용을 권장합니다:

-
# 가상환경 생성
-python -m venv atio_env
-
-# 가상환경 활성화 (Windows)
-atio_env\Scripts\activate
-
-# 가상환경 활성화 (macOS/Linux)
-source atio_env/bin/activate
-
-# Atio 설치
-pip install atio
-
-
-
-
-

Conda 사용

-

Conda를 사용하는 경우:

-
# Conda 환경 생성
-conda create -n atio_env python=3.8
-conda activate atio_env
-
-# Atio 설치
-pip install atio
-
-
-
-
-

문제 해결

-

설치 중 문제가 발생하는 경우:

-

권한 오류: -.. code-block:: bash

-
-

pip install atio –user

-
-

캐시 문제: -.. code-block:: bash

-
-

pip install atio –no-cache-dir

-
-

의존성 충돌: -.. code-block:: bash

-
-

pip install atio –force-reinstall

-
-

특정 Python 버전: -.. code-block:: bash

-
-

python3.8 -m pip install atio

-
-
-
-

업그레이드

-

최신 버전으로 업그레이드:

-
pip install --upgrade atio
-
-
-

특정 버전으로 다운그레이드:

-
pip install atio==1.0.0
-
-
-
-
-

제거

-

Atio를 제거하려면:

-
pip uninstall atio
-
-
-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv deleted file mode 100644 index 1b00a34..0000000 Binary files a/docs/build/html/objects.inv and /dev/null differ diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html deleted file mode 100644 index 2ec5a74..0000000 --- a/docs/build/html/py-modindex.html +++ /dev/null @@ -1,144 +0,0 @@ - - - - - - - - Python 모듈 목록 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - - - -
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Python 모듈 목록

- -
- a -
- - - - - - - - - - - - - - - - -
 
- a
- atio -
    - atio.core -
    - atio.plugins -
    - atio.utils -
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- -
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- - - - \ No newline at end of file diff --git a/docs/build/html/quickstart.html b/docs/build/html/quickstart.html deleted file mode 100644 index 445bdea..0000000 --- a/docs/build/html/quickstart.html +++ /dev/null @@ -1,238 +0,0 @@ - - - - - - - - - 빠른 시작 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
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- -
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빠른 시작

-

Atio를 사용하여 안전한 파일 쓰기를 시작해보세요.

-
-

기본 사용법

-

가장 간단한 사용법부터 시작해보겠습니다:

-
import atio
-import pandas as pd
-
-# 샘플 데이터 생성
-df = pd.DataFrame({
-    "name": ["Alice", "Bob", "Charlie"],
-    "age": [25, 30, 35],
-    "city": ["Seoul", "Busan", "Incheon"]
-})
-
-# 안전한 파일 쓰기
-atio.write(df, "users.parquet", format="parquet")
-
-
-

이 코드는 다음과 같은 안전장치를 제공합니다:

-
    -

  • ✅ 임시 파일에 먼저 저장

    • -

  • ✅ 저장 완료 후 원자적으로 파일 교체

    • -

  • ✅ 실패 시 원본 파일 보존

    • -

  • ✅ _SUCCESS 플래그 파일 생성

    • -
-
-
-

다양한 형식 지원

-

Atio는 다양한 데이터 형식을 지원합니다:

-
# CSV 형식
-atio.write(df, "users.csv", format="csv", index=False)
-
-# Excel 형식
-atio.write(df, "users.xlsx", format="excel", sheet_name="Users")
-
-# JSON 형식
-atio.write(df, "users.json", format="json", orient="records")
-
-# Parquet 형식 (권장)
-atio.write(df, "users.parquet", format="parquet")
-
-
-
-
-

진행률 표시

-

대용량 데이터 처리 시 진행률을 확인할 수 있습니다:

-
# 진행률 표시 활성화
-atio.write(large_df, "big_data.parquet", format="parquet", show_progress=True)
-
-
-
-
-

성능 모니터링

-

상세한 성능 정보를 확인하려면:

-
# 성능 정보 출력
-atio.write(df, "data.parquet", format="parquet", verbose=True)
-
-
-
-
-

Polars DataFrame 지원

-

Polars DataFrame도 지원합니다:

-
import polars as pl
-
-# Polars DataFrame 생성
-polars_df = pl.DataFrame({
-    "a": [1, 2, 3],
-    "b": [4, 5, 6]
-})
-
-# Polars DataFrame 저장
-atio.write(polars_df, "data.parquet", format="parquet")
-
-
-
-
-

데이터베이스 저장

-

SQL 데이터베이스에 직접 저장할 수도 있습니다:

-
from sqlalchemy import create_engine
-
-# 데이터베이스 연결
-engine = create_engine('postgresql://user:password@localhost/dbname')
-
-# 데이터베이스에 저장
-atio.write(df, format="sql", name="users", con=engine, if_exists="replace")
-
-
-
-
-

스냅샷 기능

-

데이터 버전 관리를 위한 스냅샷 기능:

-
# 스냅샷 생성
-atio.write_snapshot(df, "users", format="parquet")
-
-# 스냅샷 읽기
-df = atio.read_table("users", snapshot_id="latest")
-
-# 오래된 스냅샷 정리
-atio.expire_snapshots("users", days=30)
-
-
-
-
-

다음 단계

- -
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/html/search.html b/docs/build/html/search.html deleted file mode 100644 index 29cc9bb..0000000 --- a/docs/build/html/search.html +++ /dev/null @@ -1,129 +0,0 @@ - - - - - - - - 검색 — Atio 2.0.0 문서 - - - - - - - - - - - - - - - - - - - -
- - -
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- - - - - - - - - \ No newline at end of file diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js deleted file mode 100644 index b78a68e..0000000 --- a/docs/build/html/searchindex.js +++ /dev/null @@ -1 +0,0 @@ -Search.setIndex({"alltitles":{"API Reference":[[5,"api-reference"]],"API \ucc38\uc870":[[1,null]],"API \ucc38\uc870:":[[5,null]],"Atio Documentation":[[5,null]],"Avro":[[4,"avro"]],"CSV":[[4,"csv"],[4,"id5"]],"CSV (Comma-Separated Values)":[[4,"csv-comma-separated-values"]],"CSV \ud615\uc2dd":[[1,"csv"]],"Conda \uc0ac\uc6a9":[[6,"conda"]],"Core \ubaa8\ub4c8":[[1,"core"]],"Database":[[4,"database"]],"ETL \ud30c\uc774\ud504\ub77c\uc778":[[0,"etl"]],"Excel":[[4,"excel"],[4,"id4"]],"Excel \ud615\uc2dd":[[1,"excel"]],"HTML":[[4,"html"]],"IPC (Arrow)":[[4,"ipc-arrow"]],"Indices and tables":[[5,"indices-and-tables"]],"JSON":[[4,"json"],[4,"id3"]],"JSON \uc124\uc815 \ud30c\uc77c":[[2,"json"]],"JSON \ud615\uc2dd":[[1,"json"]],"NPY (NumPy 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