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docs: align en/ structure with main README

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- Simplify language badges (zh, en, more languages)
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TRANSLATED CONTENT:
# Postgresql Documentation Index
## Categories
### Getting Started
**File:** `getting_started.md`
**Pages:** 36
### Sql
**File:** `sql.md`
**Pages:** 460
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---
name: timescaledb
description: Manage time-series data in PostgreSQL with TimescaleDB. Use this skill to install, configure, optimize, and interact with TimescaleDB for high-performance time-series data storage and analysis. This includes creating hypertables, continuous aggregates, handling data retention, and querying time-series data efficiently.
---
# TimescaleDB Skill
Manage time-series data in PostgreSQL using TimescaleDB, extending PostgreSQL for high-performance time-series workloads.
## When to Use This Skill
Use this skill when you need to:
- Work with time-series data in PostgreSQL
- Install and configure TimescaleDB
- Create and manage hypertables
- Optimize performance for time-series data
- Implement continuous aggregates for rollup data
- Manage data retention and compression
- Query and analyze time-series data
- Migrate existing PostgreSQL tables to hypertables
- Integrate with other PostgreSQL tools and extensions
## Not For / Boundaries
This skill is NOT for:
- General PostgreSQL administration (use a specific PostgreSQL skill for that)
- Deep database tuning unrelated to time-series performance
- Replacing dedicated time-series databases if TimescaleDB's PostgreSQL foundation is not a requirement
- Providing data visualization beyond basic SQL queries (use a BI tool or separate visualization library)
## Quick Reference
### Installation & Configuration
**Install TimescaleDB Extension (Debian/Ubuntu):**
```bash
sudo apt install -y postgresql-{{pg_version}}-timescaledb
sudo pg_createcluster {{pg_version}} main --start
sudo pg_ctlcluster {{pg_version}} main start
sudo -u postgres psql -c "CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE;"
```
*(Replace `{{pg_version}}` with your PostgreSQL version, e.g., 16)*
**Configuration (postgresql.conf):**
```ini
# Add to postgresql.conf
shared_preload_libraries = 'timescaledb'
timescaledb.max_background_workers = 8 # Adjust based on CPU cores
max_connections = 100 # Adjust based on workload
```
*(After changes, restart PostgreSQL: `sudo systemctl restart postgresql`)*
### Hypertables
**Create Hypertables:**
```sql
CREATE TABLE sensor_data (
time TIMESTAMPTZ NOT NULL,
device_id INT,
temperature DOUBLE PRECISION,
humidity DOUBLE PRECISION
);
SELECT create_hypertable('sensor_data', 'time');
```
**Convert Existing Table to Hypertable:**
```sql
SELECT create_hypertable('your_existing_table', 'time_column', migrate_data => true);
```
**Show Hypertables:**
```sql
\d+
SELECT * FROM timescaledb_information.hypertables;
```
### Continuous Aggregates
**Create Continuous Aggregate:**
```sql
CREATE MATERIALIZED VIEW device_hourly_summary
WITH (timescaledb.continuous) AS
SELECT
time_bucket('1 hour', time) AS bucket,
device_id,
AVG(temperature) AS avg_temp,
MAX(temperature) AS max_temp
FROM sensor_data
GROUP BY time_bucket('1 hour', time), device_id
WITH NO DATA; -- Initially create without data
-- Refresh the continuous aggregate
CALL refresh_continuous_aggregate('device_hourly_summary', NULL, NULL);
```
**Get Continuous Aggregates Info:**
```sql
SELECT * FROM timescaledb_information.continuous_aggregates;
```
### Data Retention & Compression
**Set Data Retention Policy (Drop data older than 3 months):**
```sql
SELECT add_retention_policy('sensor_data', INTERVAL '3 months');
```
**Enable Compression (Compress data older than 7 days):**
```sql
ALTER TABLE sensor_data SET (timescaledb.compress = TRUE);
SELECT add_compression_policy('sensor_data', INTERVAL '7 days');
```
**Show Compression Status:**
```sql
SELECT * FROM timescaledb_information.compression_settings;
```
### Querying Time-Series Data
**Basic Time-Range Query:**
```sql
SELECT * FROM sensor_data
WHERE time >= NOW() - INTERVAL '1 day'
AND time < NOW()
ORDER BY time DESC;
```
**Gapfilling and Interpolation:**
```sql
SELECT
time_bucket('1 hour', time) AS bucket,
AVG(temperature) AS avg_temp,
locf(AVG(temperature)) OVER (ORDER BY time_bucket('1 hour', time)) AS avg_temp_locf
FROM sensor_data
GROUP BY bucket
ORDER BY bucket;
```
### High-Performance Queries
**Approximate Count:**
```sql
SELECT COUNT(*) FROM sensor_data TABLESAMPLE BERNOULLI (1);
```
**Top-N Queries:**
```sql
SELECT time, device_id, temperature
FROM sensor_data
WHERE time >= NOW() - INTERVAL '1 day'
ORDER BY temperature DESC
LIMIT 10;
```
## Examples
### Example 1: IoT Sensor Data Pipeline
- Input: Stream of sensor readings (time, device_id, value)
- Steps:
1. Create a hypertable for `iot_readings`.
2. Ingest data into the hypertable.
3. Create a continuous aggregate to compute hourly average readings.
4. Query the continuous aggregate for a specific device's hourly trend.
5. Set a retention policy to keep only 1 year of raw data.
- Expected output / acceptance: Efficient storage, automatic hourly rollups, and proper data pruning.
### Example 2: Financial Tick Data Analysis
- Input: High-frequency financial tick data (timestamp, symbol, price, volume)
- Steps:
1. Create a hypertable `tick_data` with proper chunk sizing for high ingest rate.
2. Enable compression for older `tick_data`.
3. Query `tick_data` to calculate 5-minute VWAP (Volume Weighted Average Price) for a specific symbol.
4. Visualize the VWAP over the last trading day.
- Expected output / acceptance: Ability to ingest and analyze millions of rows/second, with optimized storage and fast analytical queries.
### Example 3: Monitoring System Metrics
- Input: Server metrics (timestamp, host_id, cpu_usage, memory_usage, network_io)
- Steps:
1. Create a hypertable `system_metrics` partitioned by `time` and `host_id`.
2. Use a `time_bucket_gapfill` query to find CPU usage for all hosts over the last 24 hours, filling in missing data points.
3. Create an alert based on `MAX(cpu_usage)` exceeding a threshold using a continuous aggregate.
- Expected output / acceptance: Comprehensive monitoring with gap-filled data for visualization and real-time alerting.
## References
- `references/installation.md`: Detailed installation and setup
- `references/hypertables.md`: Deep dive into hypertable management
- `references/continuous_aggregates.md`: Advanced continuous aggregate techniques
- `references/compression.md`: Comprehensive guide to data compression
- `references/api.md`: TimescaleDB SQL functions and commands reference
- `references/performance.md`: Performance tuning and best practices
- `references/getting_started.md`: Official TimescaleDB Getting Started Guide
- `references/llms.md`: Using TimescaleDB with LLMs (e.g., storing embeddings, RAG)
- `references/llms-full.md`: Full LLM integration scenarios
- `references/tutorials.md`: Official TimescaleDB Tutorials and Use Cases
- `references/time_buckets.md`: Guide to `time_bucket` and gapfilling functions
- `references/hyperfunctions.md`: Advanced analytical functions for time-series
## Maintenance
- Sources: Official TimescaleDB Documentation, GitHub repository, blog posts.
- Last updated: 2025-12-17
- Known limits: This skill focuses on core TimescaleDB features. Advanced PostgreSQL features (e.g., PostGIS, JSONB) are covered by other specialized skills.
## Troubleshooting
### Slow Queries
- Ensure indexes are on `time` and other frequently queried columns.
- Verify chunk sizing is appropriate for your data ingestion rate.
- Use `EXPLAIN ANALYZE` to identify bottlenecks.
- Consider creating continuous aggregates for frequently accessed aggregated data.
### High Disk Usage
- Implement data retention policies for older, less critical data.
- Enable compression for older chunks.
- Regularly run `VACUUM ANALYZE` on your tables.
### Failed to Create Hypertable
- Ensure the `time` column is `TIMESTAMPTZ` or a supported integer type.
- The table must be empty or you must use `migrate_data => true`.
- Check for existing triggers or foreign keys that might conflict.
---
**This skill provides a robust foundation for managing time-series data with TimescaleDB!**
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# Timescaledb Documentation Index
## Categories
### Api
**File:** `api.md`
**Pages:** 100
### Compression
**File:** `compression.md`
**Pages:** 19
### Continuous Aggregates
**File:** `continuous_aggregates.md`
**Pages:** 21
### Getting Started
**File:** `getting_started.md`
**Pages:** 3
### Hyperfunctions
**File:** `hyperfunctions.md`
**Pages:** 34
### Hypertables
**File:** `hypertables.md`
**Pages:** 103
### Installation
**File:** `installation.md`
**Pages:** 37
### Other
**File:** `other.md`
**Pages:** 248
### Performance
**File:** `performance.md`
**Pages:** 2
### Time Buckets
**File:** `time_buckets.md`
**Pages:** 16
### Tutorials
**File:** `tutorials.md`
**Pages:** 12
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TRANSLATED CONTENT:
# Tiger DataDocumentation
Over 3 million Tiger Datadatabases power customer-facing applications. Speed without sacrifice for real-time analytics, time series, and vector workloads. Creators of TimescaleDB.
Tiger Cloud is the modern Postgres data platform for all your applications. It enhances Postgres to handle time series, events, real-time analytics, and vector search—all in a single database alongside transactional workloads.
You get one system that handles live data ingestion, late and out-of-order updates, and low latency queries, with the performance, reliability, and scalability your app needs. Ideal for IoT, crypto, finance, SaaS, and a myriad other domains, Tiger Cloud allows you to build data-heavy, mission-critical apps while retaining the familiarity and reliability of PostgreSQL.
This repository contains the complete documentation for Tiger Dataproducts available at https://docs.tigerdata.com/.
## Getting Started
- [Get started overview](https://docs.tigerdata.com/getting-started/latest/): Introduction to Tiger Dataproducts and services
- [Create a Tiger Cloud service](https://docs.tigerdata.com/getting-started/latest/services/): Learn about Tiger Cloud capabilities and create your first service
- [Run queries from Tiger Cloud Console](https://docs.tigerdata.com/getting-started/latest/run-queries-from-console/): Use the SQL editor and SQL Assistant in Tiger Cloud
- [Try key Tiger Datafeatures](https://docs.tigerdata.com/getting-started/latest/try-key-features-timescale-products/): Explore hypertables, time buckets, compression, and continuous aggregates
- [Start coding with TigerData](https://docs.tigerdata.com/getting-started/latest/start-coding-with-timescale/): Connect and code with your preferred programming language
## Core Features and Functionality
### Hypertables
- [About hypertables](https://docs.tigerdata.com/use-timescale/latest/hypertables/about-hypertables/): Core concept for time-series optimization
- [Create and manage hypertables](https://docs.tigerdata.com/use-timescale/latest/hypertables/hypertable-crud/): CRUD operations on hypertables
- [Improve query performance](https://docs.tigerdata.com/use-timescale/latest/hypertables/improve-query-performance/): Performance optimization techniques
- [Unique indexes on hypertables](https://docs.tigerdata.com/use-timescale/latest/hypertables/hypertables-and-unique-indexes/): Handling unique constraints
### Hypercore (Columnar Storage)
- [Hypercore overview](https://docs.tigerdata.com/use-timescale/latest/hypercore/): Advanced columnar storage for real-time analytics
- [Real-time analytics in Hypercore](https://docs.tigerdata.com/use-timescale/latest/hypercore/real-time-analytics-in-hypercore/): High-performance analytics capabilities
- [Compression methods](https://docs.tigerdata.com/use-timescale/latest/hypercore/compression-methods/): Advanced compression techniques
- [Secondary indexes](https://docs.tigerdata.com/use-timescale/latest/hypercore/secondary-indexes/): Indexing strategies for columnar data
- [Modify data in Hypercore](https://docs.tigerdata.com/use-timescale/latest/hypercore/modify-data-in-hypercore/): Data modification operations
### Continuous Aggregates
- [About continuous aggregates](https://docs.tigerdata.com/use-timescale/latest/continuous-aggregates/about-continuous-aggregates/): Materialized views for time-series data
- [Create continuous aggregates](https://docs.tigerdata.com/use-timescale/latest/continuous-aggregates/create-a-continuous-aggregate/): Implementation guide
- [Real-time aggregates](https://docs.tigerdata.com/use-timescale/latest/continuous-aggregates/real-time-aggregates/): Real-time query capabilities
- [Hierarchical continuous aggregates](https://docs.tigerdata.com/use-timescale/latest/continuous-aggregates/hierarchical-continuous-aggregates/): Multi-level aggregation
- [Refresh policies](https://docs.tigerdata.com/use-timescale/latest/continuous-aggregates/refresh-policies/): Automated refresh management
- [Compression on continuous aggregates](https://docs.tigerdata.com/use-timescale/latest/continuous-aggregates/compression-on-continuous-aggregates/): Storage optimization
### Hyperfunctions
- [About hyperfunctions](https://docs.tigerdata.com/use-timescale/latest/hyperfunctions/about-hyperfunctions/): Advanced analytical functions
- [Function pipelines](https://docs.tigerdata.com/use-timescale/latest/hyperfunctions/function-pipelines/): Chaining analytical operations
- [Statistical aggregates](https://docs.tigerdata.com/use-timescale/latest/hyperfunctions/stats-aggs/): Statistical analysis functions
- [Time-weighted averages](https://docs.tigerdata.com/use-timescale/latest/hyperfunctions/time-weighted-averages/): Time-series averaging
- [Gapfilling and interpolation](https://docs.tigerdata.com/use-timescale/latest/hyperfunctions/gapfilling-interpolation/): Handle missing data
- [Counter aggregation](https://docs.tigerdata.com/use-timescale/latest/hyperfunctions/counter-aggregation/): Monitor counter metrics
- [Approximate count distincts](https://docs.tigerdata.com/use-timescale/latest/hyperfunctions/approx-count-distincts/): Efficient distinct counting
- [Percentile approximation](https://docs.tigerdata.com/use-timescale/latest/hyperfunctions/percentile-approx/): Statistical percentile calculations
## Data Operations
### Writing Data
- [About writing data](https://docs.tigerdata.com/use-timescale/latest/write-data/about-writing-data/): Overview of data ingestion
- [Insert data](https://docs.tigerdata.com/use-timescale/latest/write-data/insert/): Insert operations and best practices
- [Update data](https://docs.tigerdata.com/use-timescale/latest/write-data/update/): Update existing records
- [Upsert data](https://docs.tigerdata.com/use-timescale/latest/write-data/upsert/): Insert or update patterns
- [Delete data](https://docs.tigerdata.com/use-timescale/latest/write-data/delete/): Data deletion strategies
### Querying Data
- [About querying data](https://docs.tigerdata.com/use-timescale/latest/query-data/about-query-data/): Query fundamentals
- [SELECT queries](https://docs.tigerdata.com/use-timescale/latest/query-data/select/): Basic and advanced SELECT operations
- [Advanced analytic queries](https://docs.tigerdata.com/use-timescale/latest/query-data/advanced-analytic-queries/): Complex analytical queries
- [SkipScan for DISTINCT](https://docs.tigerdata.com/use-timescale/latest/query-data/skipscan/): Optimized DISTINCT operations
### Time Buckets
- [About time buckets](https://docs.tigerdata.com/use-timescale/latest/time-buckets/about-time-buckets/): Time-based data grouping
- [Use time buckets](https://docs.tigerdata.com/use-timescale/latest/time-buckets/use-time-buckets/): Implementation examples
### Data Ingestion
- [Import CSV data](https://docs.tigerdata.com/use-timescale/latest/ingest-data/import-csv/): CSV data import
- [Import from MySQL](https://docs.tigerdata.com/use-timescale/latest/ingest-data/import-mysql/): MySQL migration
- [Import Parquet files](https://docs.tigerdata.com/use-timescale/latest/ingest-data/import-parquet/): Parquet data ingestion
- [Ingest from Kafka](https://docs.tigerdata.com/use-timescale/latest/ingest-data/ingest-kafka/): Apache Kafka integration
- [Ingest with Telegraf](https://docs.tigerdata.com/use-timescale/latest/ingest-data/ingest-telegraf/): Telegraf data collection
## Data Management
### Compression
- [About compression](https://docs.tigerdata.com/use-timescale/latest/compression/about-compression/): Storage optimization overview
- [Compression design](https://docs.tigerdata.com/use-timescale/latest/compression/compression-design/): Design considerations
- [Manual compression](https://docs.tigerdata.com/use-timescale/latest/compression/manual-compression/): Manual compression operations
- [Compression policies](https://docs.tigerdata.com/use-timescale/latest/compression/compression-policy/): Automated compression
- [Modify compressed data](https://docs.tigerdata.com/use-timescale/latest/compression/modify-compressed-data/): Working with compressed data
- [Modify schemas](https://docs.tigerdata.com/use-timescale/latest/compression/modify-a-schema/): Schema changes on compressed tables
### Data Retention
- [About data retention](https://docs.tigerdata.com/use-timescale/latest/data-retention/about-data-retention/): Automated data lifecycle management
- [Create retention policies](https://docs.tigerdata.com/use-timescale/latest/data-retention/create-a-retention-policy/): Policy creation and management
- [Data retention with continuous aggregates](https://docs.tigerdata.com/use-timescale/latest/data-retention/data-retention-with-continuous-aggregates/): Retention for aggregated data
- [Manually drop chunks](https://docs.tigerdata.com/use-timescale/latest/data-retention/manually-drop-chunks/): Manual data removal
### Data Tiering
- [About data tiering](https://docs.tigerdata.com/use-timescale/latest/data-tiering/about-data-tiering/): Multi-tier storage strategy
- [Enable data tiering](https://docs.tigerdata.com/use-timescale/latest/data-tiering/enabling-data-tiering/): Setup and configuration
- [Query tiered data](https://docs.tigerdata.com/use-timescale/latest/data-tiering/querying-tiered-data/): Working with tiered storage
- [Tiered data with replicas and forks](https://docs.tigerdata.com/use-timescale/latest/data-tiering/tiered-data-replicas-forks/): Advanced tiering scenarios
### Jobs and Automation
- [Create and manage jobs](https://docs.tigerdata.com/use-timescale/latest/jobs/create-and-manage-jobs/): Background job management
- [Downsample and compress example](https://docs.tigerdata.com/use-timescale/latest/jobs/example-downsample-and-compress/): Automated data processing
- [Generic retention example](https://docs.tigerdata.com/use-timescale/latest/jobs/example-generic-retention/): Custom retention policies
- [Tiered storage example](https://docs.tigerdata.com/use-timescale/latest/jobs/example-tiered-storage/): Automated tiering
## Infrastructure and Operations
### Tiger Cloud Services
- [Service overview](https://docs.tigerdata.com/use-timescale/latest/services/service-overview/): Tiger Cloud service architecture
- [Service management](https://docs.tigerdata.com/use-timescale/latest/services/service-management/): Lifecycle management
- [Service explorer](https://docs.tigerdata.com/use-timescale/latest/services/service-explorer/): Service monitoring and insights
- [Change resources](https://docs.tigerdata.com/use-timescale/latest/services/change-resources/): Scale compute and storage
- [Connection pooling](https://docs.tigerdata.com/use-timescale/latest/services/connection-pooling/): Manage database connections
### Configuration
- [About configuration](https://docs.tigerdata.com/use-timescale/latest/configuration/about-configuration/): Configuration overview
- [Customize configuration](https://docs.tigerdata.com/use-timescale/latest/configuration/customize-configuration/): Custom settings
- [Advanced parameters](https://docs.tigerdata.com/use-timescale/latest/configuration/advanced-parameters/): Advanced tuning options
### High Availability
- [High availability overview](https://docs.tigerdata.com/use-timescale/latest/ha-replicas/high-availability/): HA architecture and setup
- [Read scaling](https://docs.tigerdata.com/use-timescale/latest/ha-replicas/read-scaling/): Read replica configuration
### Backup and Restore
- [Backup and restore overview](https://docs.tigerdata.com/use-timescale/latest/backup-restore/backup-restore-cloud/): Cloud backup strategies
- [Point-in-time recovery](https://docs.tigerdata.com/use-timescale/latest/backup-restore/point-in-time-recovery/): PITR capabilities
### Security
- [Security overview](https://docs.tigerdata.com/use-timescale/latest/security/overview/): Security architecture
- [Member management](https://docs.tigerdata.com/use-timescale/latest/security/members/): User and role management
- [Multi-factor authentication](https://docs.tigerdata.com/use-timescale/latest/security/multi-factor-authentication/): MFA setup
- [SAML authentication](https://docs.tigerdata.com/use-timescale/latest/security/saml/): SSO integration
- [Client credentials](https://docs.tigerdata.com/use-timescale/latest/security/client-credentials/): Application authentication
- [Read-only role](https://docs.tigerdata.com/use-timescale/latest/security/read-only-role/): Restricted access roles
- [Strict SSL](https://docs.tigerdata.com/use-timescale/latest/security/strict-ssl/): SSL configuration
- [VPC peering](https://docs.tigerdata.com/use-timescale/latest/security/vpc/): Private network connectivity
- [Transit Gateway](https://docs.tigerdata.com/use-timescale/latest/security/transit-gateway/): Multi-cloud connectivity
- [IP allow list](https://docs.tigerdata.com/use-timescale/latest/security/ip-allow-list/): Network access control
### Schema Management
- [About schemas](https://docs.tigerdata.com/use-timescale/latest/schema-management/about-schemas/): Schema design principles
- [About indexing](https://docs.tigerdata.com/use-timescale/latest/schema-management/about-indexing/): Index strategies
- [About constraints](https://docs.tigerdata.com/use-timescale/latest/schema-management/about-constraints/): Constraint management
- [About tablespaces](https://docs.tigerdata.com/use-timescale/latest/schema-management/about-tablespaces/): Storage management
- [Alter operations](https://docs.tigerdata.com/use-timescale/latest/schema-management/alter/): Schema modifications
- [Indexing](https://docs.tigerdata.com/use-timescale/latest/schema-management/indexing/): Index creation and management
- [JSON support](https://docs.tigerdata.com/use-timescale/latest/schema-management/json/): Working with JSON data
- [Triggers](https://docs.tigerdata.com/use-timescale/latest/schema-management/triggers/): Database triggers
- [Foreign data wrappers](https://docs.tigerdata.com/use-timescale/latest/schema-management/foreign-data-wrappers/): External data integration
### Extensions
- [pgvector](https://docs.tigerdata.com/use-timescale/latest/extensions/pgvector/): Vector similarity search
- [PostGIS](https://docs.tigerdata.com/use-timescale/latest/extensions/postgis/): Geospatial data support
- [pgcrypto](https://docs.tigerdata.com/use-timescale/latest/extensions/pgcrypto/): Cryptographic functions
### Monitoring and Metrics
- [Monitoring overview](https://docs.tigerdata.com/use-timescale/latest/metrics-logging/monitoring/): System monitoring
- [AWS CloudWatch](https://docs.tigerdata.com/use-timescale/latest/metrics-logging/aws-cloudwatch/): CloudWatch integration
- [Datadog](https://docs.tigerdata.com/use-timescale/latest/metrics-logging/datadog/): Datadog monitoring
- [Prometheus metrics](https://docs.tigerdata.com/use-timescale/latest/metrics-logging/metrics-to-prometheus/): Prometheus integration
## Integrate AI with Tiger Data
- [AI overview](https://docs.tigerdata.com/ai/latest/): Integrate AI with your Tiger Data products
- [Integrate Tiger Cloud with your AI Assistant](https://docs.tigerdata.com/ai/latest/mcp-server/): Manage your services and optimize your schema and queries with your AI Assistant
- [Aggregate organizational data with AI agents](https://docs.tigerdata.com/ai/latest/tiger-eon/): Unify company knowledge with slack-native AI agents
- [Integrate a slack-native AI agent](https://docs.tigerdata.com/ai/latest/tiger-agents-for-work/): Configure a Slack-native AI agent to do what you want
- [Key vector database concepts](https://docs.tigerdata.com/ai/latest/key-vector-database-concepts-for-understanding-pgvector/): Key concepts for working with pgvector data in Postgres
- [SQL interface for pgvector](https://docs.tigerdata.com/ai/latest/sql-interface-for-pgvector-and-timescale-vector/): SQL interface for pgai, pgvector and pgvectorscale in Postgres
## Tutorials and Examples
- [Tutorials overview](https://docs.tigerdata.com/tutorials/latest/): Hands-on tutorials and examples
- [Community cookbook](https://docs.tigerdata.com/tutorials/latest/cookbook/): Code examples and recipes
- [Real-time analytics for energy consumption](https://docs.tigerdata.com/tutorials/latest/real-time-analytics-energy-consumption/): Energy data analysis
- [Real-time analytics for transport](https://docs.tigerdata.com/tutorials/latest/real-time-analytics-transport/): Transportation data analysis
- [Simulate IoT sensor data](https://docs.tigerdata.com/tutorials/latest/simulate-iot-sensor-data/): IoT data simulation
- [Ingest real-time websocket data](https://docs.tigerdata.com/tutorials/latest/ingest-real-time-websocket-data/): WebSocket data streaming
### Dataset Tutorials
- [Bitcoin blockchain analysis](https://docs.tigerdata.com/tutorials/latest/blockchain-analyze/): Analyze blockchain transactions with Hypercore
- [Financial tick data analysis](https://docs.tigerdata.com/tutorials/latest/financial-tick-data/): High-frequency financial data
- [Financial real-time ingestion](https://docs.tigerdata.com/tutorials/latest/financial-ingest-real-time/): Real-time financial data streaming
- [NYC taxi data analysis](https://docs.tigerdata.com/tutorials/latest/nyc-taxi-cab/): Time-series analysis with NYC taxi data
- [NYC taxi geospatial analysis](https://docs.tigerdata.com/tutorials/latest/nyc-taxi-geospatial/): Geospatial data visualization
- [Energy consumption analysis](https://docs.tigerdata.com/tutorials/latest/energy-data/): Energy usage patterns and optimization
## Integrations
### Cloud Platforms
- [AWS integrations](https://docs.tigerdata.com/integrations/latest/aws/): Amazon Web Services integration
- [AWS Lambda](https://docs.tigerdata.com/integrations/latest/aws-lambda/): Serverless functions
- [Amazon SageMaker](https://docs.tigerdata.com/integrations/latest/amazon-sagemaker/): Machine learning platform
- [Google Cloud](https://docs.tigerdata.com/integrations/latest/google-cloud/): Google Cloud Platform integration
- [Microsoft Azure](https://docs.tigerdata.com/integrations/latest/microsoft-azure/): Microsoft Azure integration
### Data Integration
- [Apache Kafka](https://docs.tigerdata.com/integrations/latest/apache-kafka/): Kafka streaming integration
- [Apache Airflow](https://docs.tigerdata.com/integrations/latest/apache-airflow/): Workflow orchestration
- [Debezium](https://docs.tigerdata.com/integrations/latest/debezium/): Change data capture
- [Decodable](https://docs.tigerdata.com/integrations/latest/decodable/): Real-time stream processing
- [Fivetran](https://docs.tigerdata.com/integrations/latest/fivetran/): Data pipeline automation
- [PostgreSQL](https://docs.tigerdata.com/integrations/latest/postgresql/): PostgreSQL compatibility
### Visualization and Analytics
- [Grafana](https://docs.tigerdata.com/integrations/latest/grafana/): Monitoring and visualization
- [Tableau](https://docs.tigerdata.com/integrations/latest/tableau/): Business intelligence
- [Power BI](https://docs.tigerdata.com/integrations/latest/power-bi/): Microsoft business analytics
### Development Tools
- [psql](https://docs.tigerdata.com/integrations/latest/psql/): PostgreSQL command line
- [pgAdmin](https://docs.tigerdata.com/integrations/latest/pgadmin/): PostgreSQL administration
- [DBeaver](https://docs.tigerdata.com/integrations/latest/dbeaver/): Database management tool
- [Azure Data Studio](https://docs.tigerdata.com/integrations/latest/azure-data-studio/): Microsoft database tool
- [qStudio](https://docs.tigerdata.com/integrations/latest/qstudio/): SQL analytics platform
### Monitoring and Observability
- [Prometheus](https://docs.tigerdata.com/integrations/latest/prometheus/): Monitoring and alerting
- [Datadog](https://docs.tigerdata.com/integrations/latest/datadog/): Infrastructure monitoring
- [CloudWatch](https://docs.tigerdata.com/integrations/latest/cloudwatch/): AWS monitoring service
### Infrastructure
- [Kubernetes](https://docs.tigerdata.com/integrations/latest/kubernetes/): Container orchestration
- [Terraform](https://docs.tigerdata.com/integrations/latest/terraform/): Infrastructure as code
- [Supabase](https://docs.tigerdata.com/integrations/latest/supabase/): Backend-as-a-service
- [Corporate Data Center](https://docs.tigerdata.com/integrations/latest/corporate-data-center/): On-premises connectivity
### Connection Details
- [Find connection details](https://docs.tigerdata.com/integrations/latest/find-connection-details/): Service connection information
- [Troubleshooting](https://docs.tigerdata.com/integrations/latest/troubleshooting/): Integration troubleshooting guide
## Migration and Sync
- [Migration overview](https://docs.tigerdata.com/migrate/latest/): Migration strategies and tools
- [pg_dump and restore](https://docs.tigerdata.com/migrate/latest/pg-dump-and-restore/): Traditional PostgreSQL migration
- [Live migration](https://docs.tigerdata.com/migrate/latest/live-migration/): Low-downtime migration for large databases
- [Live sync for PostgreSQL](https://docs.tigerdata.com/migrate/latest/livesync-for-postgresql/): Real-time sync from PostgreSQL
- [Live sync for S3](https://docs.tigerdata.com/migrate/latest/livesync-for-s3/): Sync data from S3 storage
- [Dual-write and backfill](https://docs.tigerdata.com/migrate/latest/dual-write-and-backfill/): Migration with zero downtime
- [Migration troubleshooting](https://docs.tigerdata.com/migrate/latest/troubleshooting/): Common migration issues
## Self-hosted TimescaleDB
### Installation
- [Self-hosted overview](https://docs.tigerdata.com/self-hosted/latest/): Installation options
- [Docker installation](https://docs.tigerdata.com/self-hosted/latest/install/installation-docker/): Docker-based deployment
- [Kubernetes installation](https://docs.tigerdata.com/self-hosted/latest/install/installation-kubernetes/): Kubernetes deployment
- [Linux installation](https://docs.tigerdata.com/self-hosted/latest/install/installation-linux/): Linux package installation
- [macOS installation](https://docs.tigerdata.com/self-hosted/latest/install/installation-macos/): macOS Homebrew/MacPorts
- [Windows installation](https://docs.tigerdata.com/self-hosted/latest/install/installation-windows/): Windows installation
- [Source installation](https://docs.tigerdata.com/self-hosted/latest/install/installation-source/): Build from source
### Configuration and Management
- [Configuration overview](https://docs.tigerdata.com/self-hosted/latest/configuration/about-configuration/): Configuration fundamentals
- [TimescaleDB configuration](https://docs.tigerdata.com/self-hosted/latest/configuration/timescaledb-config/): TimescaleDB-specific settings
- [PostgreSQL configuration](https://docs.tigerdata.com/self-hosted/latest/configuration/postgres-config/): PostgreSQL tuning
- [Docker configuration](https://docs.tigerdata.com/self-hosted/latest/configuration/docker-config/): Docker-specific configuration
- [timescaledb-tune](https://docs.tigerdata.com/self-hosted/latest/configuration/timescaledb-tune/): Automated tuning tool
- [Telemetry](https://docs.tigerdata.com/self-hosted/latest/configuration/telemetry/): Usage telemetry configuration
### Backup and Restore
- [Backup overview](https://docs.tigerdata.com/self-hosted/latest/backup-and-restore/): Self-hosted backup strategies
- [Logical backups](https://docs.tigerdata.com/self-hosted/latest/backup-and-restore/logical-backup/): pg_dump/pg_restore
- [Physical backups](https://docs.tigerdata.com/self-hosted/latest/backup-and-restore/physical/): WAL-E and pgBackRest
- [Docker and WAL-E](https://docs.tigerdata.com/self-hosted/latest/backup-and-restore/docker-and-wale/): Container backup solutions
### High Availability and Replication
- [About high availability](https://docs.tigerdata.com/self-hosted/latest/replication-and-ha/about-ha/): HA architecture
- [Configure replication](https://docs.tigerdata.com/self-hosted/latest/replication-and-ha/configure-replication/): Replication setup
### Migration
- [Entire database migration](https://docs.tigerdata.com/self-hosted/latest/migration/entire-database/): Full database migration
- [Schema then data migration](https://docs.tigerdata.com/self-hosted/latest/migration/schema-then-data/): Phased migration approach
- [Same database migration](https://docs.tigerdata.com/self-hosted/latest/migration/same-db/): In-place migration
- [Migrate from InfluxDB](https://docs.tigerdata.com/self-hosted/latest/migration/migrate-influxdb/): InfluxDB migration
### Upgrades and Maintenance
- [About upgrades](https://docs.tigerdata.com/self-hosted/latest/upgrades/about-upgrades/): Upgrade strategies
- [Major upgrades](https://docs.tigerdata.com/self-hosted/latest/upgrades/major-upgrade/): Major version upgrades
- [Minor upgrades](https://docs.tigerdata.com/self-hosted/latest/upgrades/minor-upgrade/): Minor version upgrades
- [Docker upgrades](https://docs.tigerdata.com/self-hosted/latest/upgrades/upgrade-docker/): Container upgrades
- [PostgreSQL upgrades](https://docs.tigerdata.com/self-hosted/latest/upgrades/upgrade-pg/): PostgreSQL version upgrades
- [Downgrade](https://docs.tigerdata.com/self-hosted/latest/upgrades/downgrade/): Version rollback
### Tooling
- [About timescaledb-tune](https://docs.tigerdata.com/self-hosted/latest/tooling/about-timescaledb-tune/): Performance tuning tool
- [Install toolkit](https://docs.tigerdata.com/self-hosted/latest/tooling/install-toolkit/): TimescaleDB toolkit installation
### Storage Management
- [Manage storage](https://docs.tigerdata.com/self-hosted/latest/manage-storage/): Storage and tablespace management
### Uninstallation
- [Uninstall TimescaleDB](https://docs.tigerdata.com/self-hosted/latest/uninstall/uninstall-timescaledb/): Clean removal
## Managed Service for TimescaleDB (MST)
### Getting Started
- [About MST](https://docs.tigerdata.com/mst/latest/about-mst/): Managed service overview
- [Install MST](https://docs.tigerdata.com/mst/latest/installation-mst/): Service setup and configuration
- [User management](https://docs.tigerdata.com/mst/latest/user-management/): User roles and permissions
- [Billing](https://docs.tigerdata.com/mst/latest/billing/): Pricing and billing information
### Data Operations
- [Ingest data](https://docs.tigerdata.com/mst/latest/ingest-data/): Data ingestion patterns
- [Migrate to MST](https://docs.tigerdata.com/mst/latest/migrate-to-mst/): Migration to managed service
### Infrastructure and Networking
- [Connection pools](https://docs.tigerdata.com/mst/latest/connection-pools/): Connection management
- [PostgreSQL read replicas](https://docs.tigerdata.com/mst/latest/postgresql-read-replica/): Read scaling
- [VPC peering overview](https://docs.tigerdata.com/mst/latest/vpc-peering/): Private network connectivity
- [AWS VPC peering](https://docs.tigerdata.com/mst/latest/vpc-peering/vpc-peering-aws/): Amazon VPC integration
- [AWS Transit Gateway](https://docs.tigerdata.com/mst/latest/vpc-peering/vpc-peering-aws-transit/): Multi-VPC connectivity
- [Azure VPC peering](https://docs.tigerdata.com/mst/latest/vpc-peering/vpc-peering-azure/): Microsoft Azure networking
- [GCP VPC peering](https://docs.tigerdata.com/mst/latest/vpc-peering/vpc-peering-gcp/): Google Cloud networking
### Operations and Monitoring
- [Extensions](https://docs.tigerdata.com/mst/latest/extensions/): Available PostgreSQL extensions
- [Security](https://docs.tigerdata.com/mst/latest/security/): Security configuration
- [Maintenance](https://docs.tigerdata.com/mst/latest/maintenance/): Maintenance windows and updates
- [Failover](https://docs.tigerdata.com/mst/latest/failover/): High availability failover
- [Manage backups](https://docs.tigerdata.com/mst/latest/manage-backups/): Backup management
- [View service logs](https://docs.tigerdata.com/mst/latest/viewing-service-logs/): Log access and analysis
### Tools and APIs
- [Aiven Client](https://docs.tigerdata.com/mst/latest/aiven-client/): Command-line management tool
- [REST API](https://docs.tigerdata.com/mst/latest/restapi/): Programmatic service management
- [Identify index issues](https://docs.tigerdata.com/mst/latest/identify-index-issues/): Performance optimization
### Integrations
- [MST integrations overview](https://docs.tigerdata.com/mst/latest/integrations/): Integration options
- [Grafana integration](https://docs.tigerdata.com/mst/latest/integrations/grafana-mst/): Visualization
- [Prometheus integration](https://docs.tigerdata.com/mst/latest/integrations/prometheus-mst/): Monitoring
- [Datadog metrics](https://docs.tigerdata.com/mst/latest/integrations/metrics-datadog/): Infrastructure monitoring
- [Logging integration](https://docs.tigerdata.com/mst/latest/integrations/logging/): Log management
## API Reference
### Core APIs
- [API overview](https://docs.tigerdata.com/api/latest/): Complete API reference
- [Hypertable management](https://docs.tigerdata.com/api/latest/hypertable/): Hypertable creation and management
- [Hypercore APIs](https://docs.tigerdata.com/api/latest/hypercore/): Columnar storage operations
- [Continuous aggregates](https://docs.tigerdata.com/api/latest/continuous-aggregates/): Materialized view management
- [Compression APIs](https://docs.tigerdata.com/api/latest/compression/): Data compression functions
- [Data retention](https://docs.tigerdata.com/api/latest/data-retention/): Retention policy management
- [Jobs and automation](https://docs.tigerdata.com/api/latest/jobs-automation/): Background job management
### Hyperfunctions
- [Hyperfunctions overview](https://docs.tigerdata.com/api/latest/hyperfunctions/): Advanced analytical functions
- [Statistical aggregates](https://docs.tigerdata.com/api/latest/stats-aggregates/): Statistical analysis
- [Frequency analysis](https://docs.tigerdata.com/api/latest/frequency-analysis/): Frequency and histogram functions
- [Time-weighted averages](https://docs.tigerdata.com/api/latest/time-weighted-averages/): Time-series averaging
- [Gapfilling and interpolation](https://docs.tigerdata.com/api/latest/gapfilling-interpolation/): Missing data handling
- [Counter aggregates](https://docs.tigerdata.com/api/latest/counter-aggregates/): Counter metrics
- [Gauge aggregates](https://docs.tigerdata.com/api/latest/gauge-aggregates/): Gauge metrics
- [State aggregates](https://docs.tigerdata.com/api/latest/state-aggregates/): State tracking
### Configuration and Administration
- [Configuration APIs](https://docs.tigerdata.com/api/latest/configuration/): Database configuration
- [Administration functions](https://docs.tigerdata.com/api/latest/administration/): Administrative operations
- [Informational views](https://docs.tigerdata.com/api/latest/informational-views/): System information views
## About TigerData
- [About overview](https://docs.tigerdata.com/about/latest/): Company and product information
- [Pricing and account management](https://docs.tigerdata.com/about/latest/pricing-and-account-management/): Pricing plans and billing
- [TimescaleDB editions](https://docs.tigerdata.com/about/latest/timescaledb-editions/): Product tiers and features
- [Changelog](https://docs.tigerdata.com/about/latest/changelog/): Latest product updates
- [Release notes](https://docs.tigerdata.com/about/latest/release-notes/): Version release information
- [Whitepaper](https://docs.tigerdata.com/about/latest/whitepaper/): Technical architecture paper
- [Contribute to TigerData](https://docs.tigerdata.com/about/latest/contribute-to-timescale/): Community contribution guide
## Contributing
To contribute to this documentation:
1. Fork or clone the repository
2. Create a branch from `latest`
3. Make your changes following the style guide in CONTRIBUTING.md
4. Submit a pull request back to `latest`
5. Sign the Contributor License Agreement (CLA) if this is your first contribution
The documentation is built using Gatsby and automatically generates preview links for pull requests.
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# Timescaledb - Performance
**Pages:** 2
---
## Alerting
**URL:** llms-txt#alerting
**Contents:**
- Grafana
- Other alerting tools
Early issue detecting and prevention, ensuring high availability, and performance optimization are only a few of the reasons why alerting plays a major role for modern applications, databases, and services.
There are a variety of different alerting solutions you can use in conjunction
with Tiger Cloud that are part of the Postgres ecosystem. Regardless of
whether you are creating custom alerts embedded in your applications, or using
third-party alerting tools to monitor event data across your organization, there
are a wide selection of tools available.
Grafana is a great way to visualize your analytical queries, and it has a
first-class integration with Tiger Data products. Beyond data visualization, Grafana
also provides alerting functionality to keep you notified of anomalies.
Within Grafana, you can [define alert rules][define alert rules] which are
time-based thresholds for your dashboard data (for example, "Average CPU usage
greater than 80 percent for 5 minutes"). When those alert rules are triggered,
Grafana sends a message via the chosen notification channel. Grafana provides
integration with webhooks, email and more than a dozen external services
including Slack and PagerDuty.
To get started, first download and install [Grafana][Grafana-install]. Next, add
a new [Postgres data source][PostgreSQL datasource] that points to your
Tiger Cloud service. This data source was built by Tiger Data engineers, and
it is designed to take advantage of the database's time-series capabilities.
From there, proceed to your dashboard and set up alert rules as described above.
Alerting is only available in Grafana v4.0 and later.
## Other alerting tools
Tiger Cloud works with a variety of alerting tools within the Postgres
ecosystem. Users can use these tools to set up notifications about meaningful
events that signify notable changes to the system.
Some popular alerting tools that work with Tiger Cloud include:
* [DataDog][datadog-install]
* [Nagios][nagios-install]
* [Zabbix][zabbix-install]
See the [integration guides][integration-docs] for details.
===== PAGE: https://docs.tigerdata.com/use-timescale/data-retention/ =====
---
## Improve query and upsert performance
**URL:** llms-txt#improve-query-and-upsert-performance
**Contents:**
- Segmenting and ordering data
- Improve performance in the columnstore by segmenting and ordering data
Real-time analytics applications require more than fast inserts and analytical queries. They also need high performance
when retrieving individual records, enforcing constraints, or performing upserts, something that OLAP/columnar databases
lack. This pages explains how to improve performance by segmenting and ordering data.
To improve query performance using indexes, see [About indexes][about-index] and [Indexing data][create-index].
## Segmenting and ordering data
To optimize query performance, TimescaleDB enables you to explicitly control the way your data is physically organized
in the columnstore. By structuring data effectively, queries can minimize disk reads and execute more efficiently, using
vectorized execution for parallel batch processing where possible.
<center>
<img
class="main-content__illustration"
width="80%"
src="https://assets.timescale.com/docs/images/columnstore-segmentby.png"
alt=""
/>
</center>
* **Group related data together to improve scan efficiency**: organizing rows into logical segments ensures that queries
filtering by a specific value only scan relevant data sections. For example, in the above, querying for a specific ID
is particularly fast.
* **Sort data within segments to accelerate range queries**: defining a consistent order reduces the need for post-query
sorting, making time-based queries and range scans more efficient.
* **Reduce disk reads and maximize vectorized execution**: a well-structured storage layout enables efficient batch
processing (Single Instruction, Multiple Data, or SIMD vectorization) and parallel execution, optimizing query performance.
By combining segmentation and ordering, TimescaleDB ensures that columnar queries are not only fast but also
resource-efficient, enabling high-performance real-time analytics.
### Improve performance in the columnstore by segmenting and ordering data
Ordering data in the columnstore has a large impact on the compression ratio and performance of your queries.
Rows that change over a dimension should be close to each other. As hypertables contain time-series data,
they are partitioned by time. This makes the time column a perfect candidate for ordering your data since the
measurements evolve as time goes on.
If you use `orderby` as your only columnstore setting, you get a good enough compression ratio to save a lot of
storage and your queries are faster. However, if you only use `orderby`, you always have to access your data using the
time dimension, then filter the rows returned on other criteria.
Accessing the data effectively depends on your use case and your queries. You segment data in the columnstore
to match the way you want to access it. That is, in a way that makes it easier for your queries to fetch the right data
at the right time. When you segment your data to access specific columns, your queries are optimized and yield even better performance.
For example, to access information about a single device with a specific `device_id`, you segment on the `device_id` column.
This enables you to run analytical queries on compressed data in the columnstore much faster.
For example for the following hypertable:
1. **Execute a query on a regular hypertable**
1. Query your data
Gives the following result:
1. **Execute a query on the same data segmented and ordered in the columnstore**
1. Control the way your data is ordered in the columnstore:
1. Query your data
Gives the following result:
As you see, using `orderby` and `segmentby` not only reduces the amount of space taken by your data, but also
vastly improves query speed.
The number of rows that are compressed together in a single batch (like the ones we see above) is 1000.
If your chunk does not contain enough data to create big enough batches, your compression ratio will be reduced.
This needs to be taken into account when you define your columnstore settings.
===== PAGE: https://docs.tigerdata.com/use-timescale/hypercore/modify-data-in-hypercore/ =====
**Examples:**
Example 1 (sql):
```sql
CREATE TABLE metrics (
time TIMESTAMPTZ,
user_id INT,
device_id INT,
data JSONB
) WITH (
tsdb.hypertable,
tsdb.partition_column='time'
);
```
Example 2 (sql):
```sql
SELECT device_id, AVG(cpu) AS avg_cpu, AVG(disk_io) AS avg_disk_io
FROM metrics
WHERE device_id = 5
GROUP BY device_id;
```
Example 3 (sql):
```sql
device_id | avg_cpu | avg_disk_io
-----------+--------------------+---------------------
5 | 0.4972598866221261 | 0.49820356730280524
(1 row)
Time: 177,399 ms
```
Example 4 (sql):
```sql
ALTER TABLE metrics SET (
timescaledb.enable_columnstore = true,
timescaledb.orderby = 'time',
timescaledb.segmentby = 'device_id'
);
```
---
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