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Getting started: Implement suggestions by CodeRabbit
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‎docs/start/connect.md‎

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:::
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:::{div} sd-text-muted
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Start to interact with the database for the first time.
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Start interacting with the database for the first time.
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:::
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‎docs/start/first-steps.md‎

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and memory usage, then enrich your time‑series data with JSON and text
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metadata to enable more comprehensive analysis.
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**Congratulations!**\
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You’re now onboarded and ready to explore further. Check out the other sections
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:::{rubric} Congratulations!
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:::
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You are now onboarded and ready to explore further. Check out the other sections
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in the menu to dive deeper into CrateDB’s capabilities.
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If you need assistance, also [check out our support plans].
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‎docs/start/going-further.md‎

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{ref}`Database management <administration>`
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```
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```{sd-item}
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Learn to manage your cluster with ease: configuration, sizing, performance tuning, cost optimization, monitoring and alerting, automation and REST API.
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Learn to manage your cluster with ease: configuration, sizing, performance tuning,
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and cost optimization. Explore monitoring, alerting, and automation.
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```
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‎docs/start/query/ad-hoc.md‎

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:::
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:::{grid-item}
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```sql
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SELECT *
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SELECT timestamp, message, host
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FROM logs
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WHERE service = 'auth' AND log_level = 'error'
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ORDER BY timestamp DESC
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:::
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:::{grid-item}
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```sql
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SELECT *
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SELECT timestamp, device_id, value
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FROM sensor_data
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WHERE timestamp > now() - INTERVAL '15 minutes';
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```
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```bash
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curl -sSf -u USERNAME:PASSWORD -X POST https://your.cratedb.cloud:4200/_sql \
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-H "Content-Type: application/json" \
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-d '{"stmt": "SELECT * FROM logs WHERE log_level = ? LIMIT 10", "args": ["error"]}'
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-d '{"stmt": "SELECT timestamp, message, host FROM logs WHERE log_level = ? LIMIT 10", "args": ["error"]}'
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```
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‎docs/start/query/aggregations.md‎

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:::{rubric} Use CrateDB when you need to
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:::
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* Aggregate over **high-ingestion** datasets (millions of records per hour)
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* Analyze **real-time** metrics across structured, JSON, or time-series fields
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* Build **dynamic dashboards** and run **interactive ad-hoc analytics**
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* Combine aggregations with **full-text**, **geospatial**, or **vector** filters
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- Aggregate over **high-ingestion** datasets (millions of records per hour)
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- Analyze **real-time** metrics across structured, JSON, or time-series fields
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- Build **dynamic dashboards** and run **interactive ad-hoc analytics**
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- Combine aggregations with **full-text**, **geospatial**, or **vector** filters
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:::{rubric} Benefits of using CrateDB for aggregations
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:::
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| Feature | Benefit |
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| ----------------------------- | ----------------------------------------------------------------------- |
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| Distributed SQL engine | Parallel execution across nodes ensures linear scalability |
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| Columnar storage | Reads only relevant columns for faster aggregations |
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| Real-time ingestion | Query freshly ingested data without delay |
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| Aggregations on any data type | Structured, JSON, full-text, geospatial, or vector |
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| Smart indexing | Built-in indexing plus user-defined indexes can boost performance |
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| Feature | Benefit |
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| ----------------------------- |--------------------------------------------------------------------------|
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| Distributed SQL engine | Parallel execution across nodes ensures linear scalability |
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| Columnar storage | Reads only relevant columns for faster aggregations |
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| Real-time ingestion | Query freshly ingested data without delay |
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| Aggregations on any data type | Structured, JSON, full-text, geospatial, or vector |
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| Smart indexing | Built-in indexing and configuration options that can boost performance |
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::::
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CrateDB supports a rich set of **SQL92-compliant** and extended functions for aggregation, including:
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* `COUNT()`, `SUM()`, `AVG()`, `MIN()`, `MAX()`
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* `STDDEV()`, `PERCENTILE()`, `VARIANCE()`, `TOPK()`
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* `HYPERLOGLOG_DISTINCT()`
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* Windowed and conditional aggregations via `OVER(...)` and `FILTER (WHERE ...)` clauses
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- `COUNT()`, `SUM()`, `AVG()`, `MIN()`, `MAX()`
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- `STDDEV()`, `PERCENTILE()`, `VARIANCE()`, `TOPK()`
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- `HYPERLOGLOG_DISTINCT()`
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- Windowed and conditional aggregations via `OVER(...)` and `FILTER (WHERE ...)` clauses
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To learn about the full set of functions, please visit the reference
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documentation at {ref}`crate-reference:aggregation-functions`.
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:Apache Superset: Explore multidimensional data visually
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:Tableau, Power BI, Metabase: Connect via PostgreSQL wire protocol
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These tools submit SQL queries to CrateDB, which returns pre-aggregate
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These tools submit SQL queries to CrateDB, which returns pre-aggregated
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or real-time data efficiently.
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To learn about the full set of integrations, please visit the
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documentation at {ref}`bi` and {ref}`visualization`.

‎docs/start/query/performance.md‎

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# Performance considerations
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Follow a few advises to use CrateDB optimally for maximum performance.
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Follow these tips to use CrateDB optimally for maximum performance.
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| Optimization | Description | Documentation |
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|--------------------------------|------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------|
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| Leverage indexes | Important for frequently grouped or filtered fields. <br> Fields are indexed by default. | {ref}`performance-optimization` |
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| Avoid SELECT \* | Select only the fields you need. | {ref}`performance-optimization` |
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| Use targeted filters | Narrow your search using `WHERE` clauses. <br> Use time filters especially on time-series or partitioned tables. | {ref}`crate-reference:sql_dql_where_clause` <br> {ref}`crate-reference:comparison-operators` |
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| Pre-aggregate | Use views for common queries. | {ref}`crate-reference:ddl-views` |
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| Use `DATE_BIN` or `DATE_TRUNC` | Apply time-based bucketing on time-series data to reduce data volume. | [Optimizing storage for historic time-series data] <br> [Resampling time-series data with DATE_BIN] |
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| Profile queries | Use `EXPLAIN` and `ANALYZE` to inspect performance. | {ref}`EXPLAIN <crate-reference:ref-explain>` <br> {ref}`ANALYZE <crate-reference:analyze>` |
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| Sizing & sharding | Choose partitioning and shard size wisely (e.g., daily partitions for time-based data). | {ref}`sharding-partitioning` <br> {ref}`sharding-performance` |
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| Optimization | Description | Documentation |
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|--------------------------------|------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Leverage indexes | Important for frequently grouped or filtered fields. <br> Fields are indexed by default. | {ref}`performance-optimization` |
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| Avoid SELECT \* | Select only the fields you need. | {ref}`performance-optimization` |
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| Use targeted filters | Narrow your search using `WHERE` clauses. <br> Use time filters especially on time-series or partitioned tables. | {ref}`crate-reference:sql_dql_where_clause` <br> {ref}`crate-reference:comparison-operators` |
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| Pre-aggregate | Use views for common queries. | {ref}`crate-reference:ddl-views` |
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| Use `DATE_BIN` or `DATE_TRUNC` | Apply time-based bucketing on time-series data to reduce data volume. | {ref}`DATE_BIN() <crate-reference:date-bin>` <br> {ref}`DATE_TRUNC() <crate-reference:scalar-date_trunc>` <br> [Optimizing storage for historic time-series data] <br> [Resampling time-series data with DATE_BIN] |
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| Profile queries | Use `EXPLAIN` and `ANALYZE` to inspect performance. | {ref}`EXPLAIN <crate-reference:ref-explain>` <br> {ref}`ANALYZE <crate-reference:analyze>` |
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| Sizing & sharding | Choose partitioning and shard size wisely (e.g., daily partitions for time-based data). | {ref}`sharding-partitioning` <br> {ref}`sharding-performance` |
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:::{important}

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