Skip to content

Naming things: Use "time series" instead of "time-series" #315

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Sep 16, 2025
Merged

Naming things: Use "time series" instead of "time-series" #315

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9601286
Naming things: Use "time series" instead of "time-series"
amotl Sep 16, 2025
c19c87b
Naming things: Fix remaining "time-series" as suggested by CodeRabbit
amotl Sep 16, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions docs/feature/cluster/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -171,12 +171,12 @@ Guidelines about balancing your strategy to yield the best performance for your

:::{grid-item-card}
:link: https://community.cratedb.com/t/sharding-and-partitioning-guide-for-time-series-data/737
:link-alt: Sharding and partitioning guide for time-series data
:link-alt: Sharding and partitioning guide for time series data
:padding: 3
:class-header: sd-text-center sd-fs-5 sd-align-minor-center sd-font-weight-bold
:class-body: sd-text-center2 sd-fs2-5
:class-footer: text-smaller
Sharding and partitioning guide for time-series data
Sharding and partitioning guide for time series data
^^^
A hands-on walkthrough to support you with building a sharding and partitioning
strategy for your time series data.
Expand Down
6 changes: 3 additions & 3 deletions docs/feature/search/geo/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -189,11 +189,11 @@ tutorials, or example applications.
**Getting Started with Geospatial Data in CrateDB**

Discover how to effortlessly create a table and seamlessly import weather
data into CrateDB in this video. Witness the power of CrateDB's time-series
data into CrateDB in this video. Witness the power of CrateDB's time series
query capabilities in action with a weather dataset, showcasing the dynamic
schema flexibility.

[CrateDB: Querying Multi-Model Heterogeneous Time-Series Data with SQL]
[CrateDB: Querying Multi-Model Heterogeneous Time Series Data with SQL]

Dive deeper into CrateDB's multi-modal features with demonstrations on
handling JSON, geospatial data, and conducting full-text searches.
Expand Down Expand Up @@ -263,7 +263,7 @@ flip dot sign. The software is written in JavaScript and runs on Node.js.

[Apache Solr Spatial Search]: https://solr.apache.org/guide/solr/latest/query-guide/spatial-search.html
[Berlin and Geo Shapes in CrateDB]: https://cratedb.com/blog/geo-shapes-in-cratedb
[CrateDB: Querying Multi-Model Heterogeneous Time-Series Data with SQL]: https://cratedb.com/resources/videos/unleashing-the-power-of-multi-model-data-querying-heterogeneous-time-series-data-with-sql-in-cratedb
[CrateDB: Querying Multi-Model Heterogeneous Time Series Data with SQL]: https://cratedb.com/resources/videos/unleashing-the-power-of-multi-model-data-querying-heterogeneous-time-series-data-with-sql-in-cratedb
[GeoJSON]: https://en.wikipedia.org/wiki/GeoJSON
[Geometric Shapes Indexing with BKD-trees]: https://cratedb.com/blog/geometric-shapes-indexing-with-bkd-trees
[Geospatial Indexing & Search at Scale with Lucene]: https://portal.ogc.org/files/?artifact_id=90337
Expand Down
6 changes: 3 additions & 3 deletions docs/home/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ orphan: true
# Welcome to CrateDB

CrateDB is a **distributed SQL database** designed for **real-time analytics
and search** at scale. Whether you are working with time-series data, full-text
and search** at scale. Whether you are working with time series data, full-text
search, or large volumes of structured and semi-structured data, CrateDB gives
you the **power of SQL**, the **scalability of NoSQL**, and the **flexibility
of a modern data platform**.
Expand All @@ -26,7 +26,7 @@ CrateDB was built for speed, scale, and simplicity:
* **Real-time performance:** Query millions of records per second with sub-second response times.
* **AI/ML-ready:** Store and serve data for modern AI pipelines.
* **Search + SQL**: Combine full-text search with rich SQL queries.
* **Geospatial & time-series**: Native support for IoT, sensor data, and location-based use cases.
* **Geospatial & time series**: Native support for IoT, sensor data, and location-based use cases.
* **Horizontal scalability**: Add nodes effortlessly to handle more data and users.
* **Resilient and fault-tolerant**: Built-in replication and recovery.
::::
Expand Down Expand Up @@ -60,7 +60,7 @@ real-time analytics and hybrid search applications that leverage CrateDB's
unique features.

* In a unified data platform, CrateDB lets you analyze relational, JSON,
time-series, geospatial, full-text, and vector data in a single system,
time series, geospatial, full-text, and vector data in a single system,
eliminating the need for multiple databases.
* The fully distributed SQL query engine, built on top of Apache Lucene,
and inheriting technologies from Elasticsearch/OpenSearch, provides performant
Expand Down
4 changes: 2 additions & 2 deletions docs/integrate/kafka/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ Apache Kafka is a widely used open-source distributed event-store and streaming

## Overview

[Apache Kafka] is a distributed event log for high-throughput, durable, and scalable data streams. CrateDB is a distributed SQL database optimized for time-series, IoT, and analytics at scale. Together, they form a robust pipeline for moving operational events from producers into a queryable store with SQL and real-time analytics.
[Apache Kafka] is a distributed event log for high-throughput, durable, and scalable data streams. CrateDB is a distributed SQL database optimized for time series, IoT, and analytics at scale. Together, they form a robust pipeline for moving operational events from producers into a queryable store with SQL and real-time analytics.

## Benefits of CrateDB + Apache Kafka

Expand Down Expand Up @@ -62,7 +62,7 @@ The processed results are then written into CrateDB, where they’re immediately

## Typical use cases

* **Time-series pipelines (sensors, logs, metrics, events)**
* **Time series pipelines (sensors, logs, metrics, events)**

Stream high-volume data from IoT devices, application logs, or monitoring systems into Kafka, then land it in CrateDB for storage and real-time querying. Ideal for scenarios where you need to keep years of historical data but still run live analytics on the latest events.
* **CDC / operational data feeds (Debezium → Kafka → CrateDB)**
Expand Down
6 changes: 3 additions & 3 deletions docs/integrate/superset/index.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,7 @@ Introduction to time‑series visualization in CrateDB and Apache Superset.
**Apache Superset 101**

From connecting databases to building charts, dashboards, and interactive filters,
this video educates about at all the basic surfaces and workflows of Apache Superset.
this video covers all the basic surfaces and workflows of Apache Superset.
:::

:::{grid-item}
Expand All @@ -119,11 +119,11 @@ this video educates about at all the basic surfaces and workflows of Apache Supe

:::{grid-item}
:columns: auto auto 8 8
**Apache Superset and CrateDB: Introduction to Time-Series Visualization**
**Apache Superset and CrateDB: Introduction to Time Series Visualization**

In this webinar, we will discuss how to use different visualization options in
Superset coupled with a SQL interface to derive interesting insights and findings
from the time-series dataset.
from the time series dataset.

- [Introduction to time series visualization in CrateDB and Apache Superset (Webinar)]
:::
Expand Down
6 changes: 3 additions & 3 deletions docs/start/first-steps.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,17 +29,17 @@ or run them directly in the CrateDB Cloud Console.

Learn how to use CrateDB’s full‑text search to explore a large dataset and manage a Netflix title catalog.

* **Exploring time-series data?** Investigate weather data — see {{ '{}(#timeseries-querying)'.format(tutorial) }}
* **Exploring time series data?** Investigate weather data — see {{ '{}(#timeseries-querying)'.format(tutorial) }}

In this tutorial, you’ll work with weather readings from multiple locations
to learn how to efficiently store and analyze time-series datasets.
to learn how to efficiently store and analyze time series datasets.

## 3. Take an advanced tutorial

* **Analyze device readings** with metadata integration — see {{ '{}(#timeseries-objects)'.format(tutorial) }}

In this tutorial, capture device metrics such as battery level, CPU usage,
and memory usage, then enrich your time‑series data with JSON and text
and memory usage, then enrich your time series data with JSON and text
metadata to enable more comprehensive analysis.


Expand Down
6 changes: 3 additions & 3 deletions docs/start/modelling/fulltext.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
CrateDB offers **native full-text search** powered by **Apache Lucene** and Okapi
BM25 ranking, accessible via SQL for easy modelling and querying of large-scale
textual data. It supports fuzzy matching, multi-language analysis, and composite
indexing, while fully integrating with data types such as JSON, time-series,
indexing, while fully integrating with data types such as JSON, time series,
geospatial, vectors, and more for comprehensive multi-model queries. Whether you
need document search, catalog lookup, or content analytics, CrateDB is an ideal
solution.
Expand Down Expand Up @@ -151,8 +151,8 @@ constraints, all in one.
walkthrough of full-text search capabilities.
* Reference Manual:
* {ref}`Full-text indices <crate-reference:fulltext-indices>`: Defining
indices, extending builtin analyzers, custom analyzers.
* {ref}`Full-text analyzers <crate-reference:sql-analyzer>`: Builtin
indices, extending built-in analyzers, custom analyzers.
* {ref}`Full-text analyzers <crate-reference:sql-analyzer>`: Built-in
analyzers, tokenizers, token and char filters.
* {ref}`SQL MATCH predicate <crate-reference:sql_dql_fulltext_search>`:
Details about MATCH predicate arguments and options.
Expand Down
4 changes: 2 additions & 2 deletions docs/start/modelling/geospatial.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,15 +4,15 @@
CrateDB supports **real-time geospatial analytics at scale**, enabling you to
store, query, and analyze 2D location-based data using standard SQL over two
dedicated types: **GEO\_POINT** and **GEO\_SHAPE**. You can seamlessly combine
spatial data with full-text, vector, JSON, or time-series in the same SQL
spatial data with full-text, vector, JSON, or time series in the same SQL
queries.

The strength of CrateDB's support for geospatial data includes:

* Designed for **real-time geospatial tracking and analytics** (e.g., fleet
tracking, mapping, location-layered apps)
* **Unified SQL platform**: spatial data can be combined with full-text search,
JSON, vectors, time-series — in the same table or query
JSON, vectors, time series — in the same table or query
* **High ingest and query throughput**, suitable for large-scale location-based
workloads

Expand Down
2 changes: 1 addition & 1 deletion docs/start/modelling/relational.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ CrateDB is a **distributed SQL database** that offers rich **relational data
modelling** with the flexibility of dynamic schemas and the scalability of NoSQL
systems. It supports **primary keys,** **joins**, **aggregations**, and
**subqueries**, just like traditional RDBMS systems—while also enabling hybrid
use cases with time-series, geospatial, full-text, vector search, and
use cases with time series, geospatial, full-text, vector search, and
semi-structured data.

Use CrateDB when you need to scale relational workloads horizontally while
Expand Down
2 changes: 1 addition & 1 deletion docs/start/modelling/timeseries.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,7 @@ ORDER BY
expected_time;
```

### Typical time-series functions
### Typical time series functions

* **Time extraction:** `date_trunc(...)`, `extract(...)`, `date_part(...)`, `now()`, `current_timestamp`
* **Time bucketing:** `date_bin()`, `interval`, `age()`
Expand Down
2 changes: 1 addition & 1 deletion docs/start/query/ad-hoc.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -237,7 +237,7 @@ Learn more about how to use ad-hoc queries effectively.
| Feature | Description | Documentation |
|---------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------|
| Dynamic schemas & <br> object columns | Flexible modeling of semi-structured JSON data <br> No need to predefine every field | {ref}`object` <br> {ref}`crate-reference:data-types-objects` |
| Time-series support | Perfect for time-bound diagnostics | {ref}`timeseries` |
| Time series support | Perfect for time-bound diagnostics | {ref}`timeseries` |
| Intelligent indexing | Works out of the box for ad-hoc querying | {ref}`search-overview` |
| Full-text & filter | Combine keyword search with structured queries | {ref}`fts` <br> {ref}`crate-reference:fulltext-indices` |

Expand Down
4 changes: 2 additions & 2 deletions docs/start/query/aggregations.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ Whether you are monitoring sensor networks, analyzing customer behavior, or powe
:::

- Aggregate over **high-ingestion** datasets (millions of records per hour)
- Analyze **real-time** metrics across structured, JSON, or time-series fields
- Analyze **real-time** metrics across structured, JSON, or time series fields
- Build **dynamic dashboards** and run **interactive ad-hoc analytics**
- Combine aggregations with **full-text**, **geospatial**, or **vector** filters

Expand Down Expand Up @@ -207,7 +207,7 @@ GROUP BY status;

CrateDB integrates seamlessly with:

:Grafana: Build real-time dashboards with time-series aggregations
:Grafana: Build real-time dashboards with time series aggregations
:Apache Superset: Explore multidimensional data visually
:Tableau, Power BI, Metabase: Connect via PostgreSQL wire protocol

Expand Down
22 changes: 11 additions & 11 deletions docs/start/query/performance.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,21 +5,21 @@
Follow these tips to use CrateDB optimally for maximum performance.
:::

| Optimization | Description | Documentation |
|--------------------------------|--------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Leverage indexes | Important for frequently grouped or filtered fields. <br> Fields are indexed by default. | {ref}`performance-optimization` |
| Avoid SELECT \* | Select only the fields you need. | {ref}`performance-optimization` |
| 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` |
| Pre-aggregate | Maintain rollup tables for common queries; use views as convenient wrappers (views are virtual, not precomputed). | |
| 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] |
| Profile queries | Use `EXPLAIN` and `ANALYZE` to inspect performance. | {ref}`EXPLAIN <crate-reference:ref-explain>` <br> {ref}`ANALYZE <crate-reference:analyze>` |
| Sizing & sharding | Choose partitioning and shard size wisely (e.g., daily partitions for time-based data). | {ref}`sharding-partitioning` <br> {ref}`sharding-performance` |
| Optimization | Description | Documentation |
|--------------------------------|-------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Leverage indexes | Important for frequently grouped or filtered fields. <br> Fields are indexed by default. | {ref}`performance-optimization` |
| Avoid SELECT \* | Select only the fields you need. | {ref}`performance-optimization` |
| 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` |
| Pre-aggregate | Maintain rollup tables for common queries; use views as convenient wrappers (views are virtual, not precomputed). | |
| 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] |
| Profile queries | Use `EXPLAIN` and `ANALYZE` to inspect performance. | {ref}`EXPLAIN <crate-reference:ref-explain>` <br> {ref}`ANALYZE <crate-reference:analyze>` |
| Sizing & sharding | Choose partitioning and shard size wisely (e.g., daily partitions for time-based data). | {ref}`sharding-partitioning` <br> {ref}`sharding-performance` |


:::{important}
For in-depth details about performance aspects, please head over to the {ref}`performance`.
:::


[Optimizing storage for historic time-series data]: https://community.cratedb.com/t/optimizing-storage-for-historic-time-series-data/762
[Resampling time-series data with DATE_BIN]: https://community.cratedb.com/t/resampling-time-series-data-with-date-bin/1009
[Optimizing storage for historic time series data]: https://community.cratedb.com/t/optimizing-storage-for-historic-time-series-data/762
[Resampling time series data with DATE_BIN]: https://community.cratedb.com/t/resampling-time-series-data-with-date-bin/1009