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Alternatives to TimescaleTime-Series Analysis in RAWS Time-Series Database: Understanding Your OptionsWhat Is a Time Series and How Is It Used?Is Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleWhy Consider Using PostgreSQL for Time-Series Data?How to Work With Time Series in Python?Tools for Working With Time-Series Analysis in PythonGuide to Time-Series Analysis in PythonTime-Series Analysis and Forecasting With Python Understanding Database Workloads: Variable, Bursty, and Uniform PatternsThe Best Time-Series Databases ComparedUnderstanding Autoregressive Time-Series ModelingStationary Time-Series AnalysisCreating a Fast Time-Series Graph With Postgres Materialized ViewsWhat Are Open-Source Time-Series Databases—Understanding Your OptionsWhat Is Temporal Data?
Optimizing Your Database: A Deep Dive into PostgreSQL Data TypesHow to Install PostgreSQL on LinuxHow to Install PostgreSQL on MacOS5 Common Connection Errors in PostgreSQL and How to Solve ThemHow to Fix No Partition of Relation Found for Row in Postgres DatabasesHow to Fix Transaction ID Wraparound ExhaustionUnderstanding PostgreSQL Date and Time FunctionsData Partitioning: What It Is and Why It MattersWhat Is Data Compression and How Does It Work?Self-Hosted or Cloud Database? A Countryside Reflection on Infrastructure ChoicesUnderstanding ACID Compliance Understanding percentile_cont() and percentile_disc() in PostgreSQLUsing PostgreSQL UPDATE With JOINUnderstanding PostgreSQL Conditional FunctionsUnderstanding PostgreSQL Array FunctionsWhat Characters Are Allowed in PostgreSQL Strings?Understanding PostgreSQL's COALESCE FunctionWhat Is Data Transformation, and Why Is It Important?Understanding PostgreSQL User-Defined FunctionsStructured vs. Semi-Structured vs. Unstructured Data in PostgreSQLUnderstanding SQL Aggregate FunctionsUnderstanding Foreign Keys in PostgreSQLUnderstanding PostgreSQLUnderstanding FROM in PostgreSQL (With Examples)Understanding FILTER in PostgreSQL (With Examples)How to Address ‘Error: Could Not Resize Shared Memory Segment’ Understanding HAVING in PostgreSQL (With Examples)Understanding GROUP BY in PostgreSQL (With Examples)Understanding LIMIT in PostgreSQL (With Examples)Understanding PostgreSQL FunctionsUnderstanding ORDER BY in PostgreSQL (With Examples)Understanding WINDOW in PostgreSQL (With Examples)Understanding PostgreSQL WITHIN GROUPPostgreSQL Mathematical Functions: Enhancing Coding EfficiencyUnderstanding DISTINCT in PostgreSQL (With Examples)Using PostgreSQL String Functions for Improved Data AnalysisData Processing With PostgreSQL Window FunctionsUnderstanding WHERE in PostgreSQL (With Examples)PostgreSQL Joins : A SummaryUnderstanding OFFSET in PostgreSQL (With Examples)Understanding the Postgres string_agg FunctionWhat Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Cross Join?What Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?PostgreSQL Join Type TheoryUnderstanding PostgreSQL SELECTA Guide to PostgreSQL ViewsStrategies for Improving Postgres JOIN PerformanceUnderstanding the Postgres extract() FunctionUnderstanding the rank() and dense_rank() Functions in PostgreSQL
Top PostgreSQL Drivers for PythonPostgreSQL Performance Tuning: Optimizing Database IndexesDetermining the Optimal Postgres Partition SizeBest Practices for (Time-)Series Metadata Tables Guide to Postgres Data ManagementHow to Query JSONB in PostgreSQLHow to Index JSONB Columns in PostgreSQLHow to Monitor and Optimize PostgreSQL Index PerformanceOptimizing Array Queries With GIN Indexes in PostgreSQLSQL/JSON Data Model and JSON in SQL: A PostgreSQL PerspectiveHow to Query JSON Metadata in PostgreSQLA Guide to pg_restore (and pg_restore Example)Handling Large Objects in PostgresPostgreSQL Performance Tuning: Designing and Implementing Your Database SchemaGuide to PostgreSQL PerformancePostgreSQL Performance Tuning: Key ParametersHow to Reduce Bloat in Large PostgreSQL TablesGuide to PostgreSQL Database OperationsPostgreSQL Performance Tuning: How to Size Your DatabaseExplaining PostgreSQL EXPLAINA Guide to Data Analysis on PostgreSQLHow PostgreSQL Data Aggregation WorksBuilding a Scalable DatabaseA Guide to Scaling PostgreSQLPg_partman vs. Hypertables for Postgres PartitioningHow to Use PostgreSQL for Data TransformationWhen to Consider Postgres PartitioningDesigning Your Database Schema: Wide vs. Narrow Postgres TablesRecursive Query in SQL: What It Is, and How to Write OneGuide to PostgreSQL Database DesignWhat Is Audit Logging and How to Enable It in PostgreSQLGuide to PostgreSQL SecurityNavigating Growing PostgreSQL Tables With Partitioning (and More)An Intro to Data Modeling on PostgreSQLBest Practices for Time-Series Data Modeling: Single or Multiple Partitioned Table(s) a.k.a. Hypertables What Is a PostgreSQL Temporary View?A PostgreSQL Database Replication GuideUnderstanding PostgreSQL TablespacesHow to Compute Standard Deviation With PostgreSQLHow to Use Psycopg2: The PostgreSQL Adapter for Python
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Understanding IoT (Internet of Things)Storing IoT Data: 8 Reasons Why You Should Use PostgreSQLHow to Choose an IoT DatabaseHow to Simulate a Basic IoT Sensor Dataset on PostgreSQLFrom Ingest to Insights in Milliseconds: Everactive's Tech Transformation With TimescaleHow Ndustrial Is Providing Fast Real-Time Queries and Safely Storing Client Data With 97 % CompressionA Beginner’s Guide to IIoT and Industry 4.0Why You Should Use PostgreSQL for Industrial IoT DataHow Hopthru Powers Real-Time Transit Analytics From a 1 TB Table Migrating a Low-Code IoT Platform Storing 20M Records/DayMoving Past Legacy Systems: Data Historian vs. Time-Series DatabaseHow United Manufacturing Hub Is Introducing Open Source to ManufacturingBuilding IoT Pipelines for Faster Analytics With IoT CoreVisualizing IoT Data at Scale With Hopara and TimescaleDB
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What InfluxDB Got Wrong5 InfluxDB Alternatives for Your Time-Series Data8 Reasons to Choose Timescale as Your InfluxDB Alternative InfluxQL, Flux, and SQL: Which Query Language Is Best? (With Cheatsheet)TimescaleDB vs. InfluxDB: Purpose Built Differently for Time-Series Data
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Published at Mar 6, 2024

PostgreSQL Extensions: pg_prewarm

​pg_prewarm is a PostgreSQL extension that allows developers to load relation data into either the operating system's buffer cache or PostgreSQL's shared buffers. This is particularly useful for reducing the time to warm up the cache after a database restart, hence the name 'pg_prewarm.'​

Installing the pg_prewarm Extension

​To install the pg_prewarm extension, you need to have PostgreSQL installed on your system. Once you have PostgreSQL, follow these steps:

1. Open your PostgreSQL command line interface.

2. Connect to the database where you want to install the extension.

3. Run the following command: CREATE EXTENSION pg_prewarm; ​

This command will install the pg_prewarm extension in your current database.

​Using the pg_prewarm Extension

​Once you have installed the pg_prewarm extension, you can use it to prewarm tables in your database, which you know you will be read soon. Here's how:

1. Connect to your database.

2. Run the following command:

SELECT pg_prewarm('your_table_name');

Replace 'your_table_name' with the name of the table you want to prewarm. This command will load the data from the specified table into the cache.

Time-series use cases for the pg_prewarm extension

The pg_prewarm extension is handy in time-series databases where data is continuously added chronologically. By preloading the most recent chunks of a hypertable into the cache after a restart, you can significantly reduce the time to execute queries on these tables.

Using pg_prewarm with Timescale and time-series data

​If you're using Timescale, a time-series cloud database built on PostgreSQL, you can use the pg_prewarm extension to improve the performance of your database. Here's how:

1. Install the pg_prewarm extension in your Timescale database. On Timescale, you can find available extensions by going to Operations > Extensions from your service overview, which will also give you installation instructions.

2. Use the pg_prewarm function to prewarm your hypertables.

SELECT pg_prewarm(format('%s.%s', chunk_schema, chunk_name)) FROM   (SELECT * FROM timescaledb_information.chunks     WHERE hypertable_name = 'your_hypertable_name' AND                   range_end > now() - interval '1 day   ) a;

​Replace 'your_hypertable_name' with the name of your hypertable and ‘1 day’ with the lookback period to match chunks. In this example, you will prewarm any chunks that cover the last day.

This command will load the data from the specified hypertable chunks into the cache, improving the performance of your queries on the most recent day of data in the hypertable.

Learn More

Looking to learn more about extending PostgreSQL for scale and times-series scenarios? Check out the tutorials in the Timescale documentation to get started.

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