TigerData logo
TigerData logo
  • Product

    Tiger Cloud

    Robust elastic cloud platform for startups and enterprises

    Agentic Postgres

    Postgres for Agents

    TimescaleDB

    Postgres for time-series, real-time analytics and events

  • Docs
  • Pricing

    Pricing

    Enterprise Tier

  • Developer Hub

    Changelog

    Benchmarks

    Blog

    Community

    Customer Stories

    Events

    Support

    Integrations

    Launch Hub

  • Company

    Contact us

    About

    Timescale

    Partners

    Security

    Careers

Log InTry for free
Home
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
Best Practices for Scaling PostgreSQLBest Practices for PostgreSQL Database OperationsHow to Store Video in PostgreSQL Using BYTEAHow to Handle High-Cardinality Data in PostgreSQLHow to Use PostgreSQL for Data NormalizationTesting Postgres Ingest: INSERT vs. Batch INSERT vs. COPYBest Practices for Postgres SecurityBest Practices for Postgres Data ManagementBest Practices for Postgres PerformanceHow to Design Your PostgreSQL Database: Two Schema ExamplesHow to Manage Your Data With Data Retention PoliciesBest Practices for PostgreSQL Data AnalysisBest Practices for PostgreSQL AggregationBest Practices for Postgres Database ReplicationHow to Use a Common Table Expression (CTE) in SQL
PostgreSQL Extensions: Unlocking Multidimensional Points With Cube PostgreSQL Extensions: hstorePostgreSQL Extensions: ltreePostgreSQL Extensions: pg_prewarmPostgreSQL Extensions: pgRoutingPostgreSQL Extensions: Using PostGIS and Timescale for Advanced Geospatial InsightsPostgreSQL Extensions: Turning PostgreSQL Into a Vector Database With pgvectorPostgreSQL Extensions: amcheckPostgreSQL Extensions: Secure Your Time-Series Data With pgcryptoPostgreSQL Extensions: pg_stat_statementsPostgreSQL Extensions: Database Testing With pgTAPPostgreSQL Extensions: Install pg_trgm for Data MatchingPostgreSQL Extensions: PL/pgSQLPostgreSQL Extensions: Intro to uuid-ossp
PostgreSQL as a Real-Time Analytics DatabaseHow to Build an IoT Pipeline for Real-Time Analytics in PostgreSQLHow to Choose a Real-Time Analytics DatabaseUnderstanding OLTPOLAP Workloads on PostgreSQL: A GuideHow to Choose an OLAP DatabaseData Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)What Is the Best Database for Real-Time AnalyticsColumnar Databases vs. Row-Oriented Databases: Which to Choose?
A Brief History of AI: How Did We Get Here, and What's Next?Text-to-SQL: A Developer’s Zero-to-Hero GuideA Beginner’s Guide to Vector EmbeddingsPostgreSQL as a Vector Database: A Pgvector TutorialUsing Pgvector With PythonHow to Choose a Vector DatabaseVector Databases Are the Wrong AbstractionUnderstanding DiskANNStreaming DiskANN: How We Made PostgreSQL as Fast as Pinecone for Vector DataA Guide to Cosine SimilarityImplementing Cosine Similarity in PythonVector Database Basics: HNSWVector Database Options for AWSVector Store vs. Vector Database: Understanding the ConnectionPgvector vs. Pinecone: Vector Database Performance and Cost ComparisonHow to Build LLM Applications With Pgvector Vector Store in LangChainHow to Implement RAG With Amazon Bedrock and LangChainRetrieval-Augmented Generation With Claude Sonnet 3.5 and PgvectorPostgreSQL Hybrid Search Using Pgvector and CohereWhat Is Vector Search? Vector Search vs Semantic SearchNearest Neighbor Indexes: What Are IVFFlat Indexes in Pgvector and How Do They WorkRAG Is More Than Just Vector SearchImplementing Filtered Semantic Search Using Pgvector and JavaScriptRefining Vector Search Queries With Time Filters in Pgvector: A TutorialUnderstanding Semantic SearchBuilding an AI Image Gallery With OpenAI CLIP, Claude Sonnet 3.5, and PgvectorWhen Should You Use Full-Text Search vs. Vector Search?HNSW vs. DiskANN
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
What Is ClickHouse and How Does It Compare to PostgreSQL and TimescaleDB for Time Series?Timescale vs. Amazon RDS PostgreSQL: Up to 350x Faster Queries, 44 % Faster Ingest, 95 % Storage Savings for Time-Series DataWhat We Learned From Benchmarking Amazon Aurora PostgreSQL ServerlessTimescaleDB vs. Amazon Timestream: 6,000x Higher Inserts, 5-175x Faster Queries, 150-220x CheaperHow to Store Time-Series Data in MongoDB and Why That’s a Bad IdeaPostgreSQL + TimescaleDB: 1,000x Faster Queries, 90 % Data Compression, and Much MoreEye or the Tiger: Benchmarking Cassandra vs. TimescaleDB for Time-Series Data
Alternatives to RDSWhy Is RDS so Expensive? Understanding RDS Pricing and CostsEstimating RDS CostsHow to Migrate From AWS RDS for PostgreSQL to TimescaleAmazon Aurora vs. RDS: Understanding the Difference
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
How to Migrate Your Data to Timescale (3 Ways)Postgres TOAST vs. Timescale CompressionBuilding Python Apps With PostgreSQL: A Developer's GuideMore Time-Series Data Analysis, Fewer Lines of Code: Meet HyperfunctionsTimescale Tips: Testing Your Chunk SizeIs Postgres Partitioning Really That Hard? An Introduction To HypertablesPostgreSQL Materialized Views and Where to Find Them5 Ways to Monitor Your PostgreSQL DatabaseData Visualization in PostgreSQL With Apache Superset
Postgres cheat sheet
HomeTime series basicsPostgres basicsPostgres guidesPostgres best practicesPostgres extensionsPostgres for real-time analytics
Sections
PostgreSQL Extensions: amcheckPostgreSQL Extensions: Unlocking Multidimensional Points With Cube PostgreSQL Extensions: hstorePostgreSQL Extensions: ltreePostgreSQL Extensions: Secure Your Time-Series Data With pgcryptoPostgreSQL Extensions: pg_prewarmPostgreSQL Extensions: pgRoutingPostgreSQL Extensions: pg_stat_statementsPostgreSQL Extensions: Database Testing With pgTAPPostgreSQL Extensions: Install pg_trgm for Data MatchingPostgreSQL Extensions: PL/pgSQLPostgreSQL Extensions: Using PostGIS and Timescale for Advanced Geospatial InsightsPostgreSQL Extensions: Intro to uuid-osspPostgreSQL Extensions: Turning PostgreSQL Into a Vector Database With pgvector

Products

Time Series and Analytics AI and Vector Enterprise Plan Cloud Status Support Security Cloud Terms of Service

Learn

Documentation Blog Forum Tutorials Changelog Success Stories Time Series Database

Company

Contact Us Careers About Brand Community Code Of Conduct Events

Subscribe to the Tiger Data Newsletter

By submitting, you acknowledge Tiger Data's Privacy Policy

2025 (c) Timescale, Inc., d/b/a Tiger Data. All rights reserved.

Privacy preferences
LegalPrivacySitemap

Published at Mar 6, 2024

Database Schema

PostgreSQL Extensions: hstore

​hstore is a powerful and flexible PostgreSQL extension that allows developers to use the database as a schema-less NoSQL store while still retaining the benefits of a traditional relational database.

It provides the ability to store key-value pairs in a single PostgreSQL value. This is particularly useful for storing semi-structured data and datasets where the number of attributes (keys) can vary from record to record.

​Installing the hstore Extension

​Before you can use the hstore extension, you need to install it. Here's how:

1. First, connect to the PostgreSQL database where you want to install the hstore extension. You can do this using the psql command-line client or a graphical client like pgAdmin. On Timescale, you can find available extensions by going to Operations > Extensions from your service overview, which will also give you installation instructions.​

2. Once connected, run the following SQL command:

CREATE EXTENSION IF NOT EXISTS hstore;​

This command will install the hstore extension if it's not already installed.

Using the hstore Extension

To use the hstore extension, you need to create a column of type hstore in your table. Here's an example:

CREATE TABLE products ( id serial PRIMARY KEY, attributes hstore ); In this example, the attributes column can store any number of key-value pairs. You can insert data into the attributes column like this:

INSERT INTO products (attributes) VALUES ('"color"=>"blue", "size"=>"XL"'); You can also query the data in an hstore column using various operators and functions provided by the hstore extension. For example, you can find all products that have a "color" key like this:

SELECT * FROM products WHERE attributes ? 'color';

​Time-Series Use Cases for hstore​

The hstore extension is particularly useful for time-series data where the structure of the data can change over time. For example, you might be tracking various metrics for a set of machines, and the set of metrics can change as you add or remove sensors from the machines.​

With hstore, you can easily add or remove attributes without having to change the schema of your database. This makes it a great fit for time-series data.

​Using hstore with Timescale and time-series data

​Timescale is a time-series database built on top of PostgreSQL. It provides advanced features for handling time-series data, like automatic partitioning and aggregation.​

You can use the hstore extension with Timescale to store time-series data with a flexible schema. Just like with a regular PostgreSQL database, you can create a table with an hstore column and insert your time-series data into it.

Here's an example:

CREATE TABLE machine_metrics ( time TIMESTAMPTZ NOT NULL, machine_id int NOT NULL, metrics hstore, PRIMARY KEY(time, machine_id) );

In this example, the metrics column can store any number of key-value pairs, allowing you to easily add or remove metrics as needed.

​hstore also works on hypertables, which allows you to get all of the functionality of TimescaleDB with the flexibility of hstore. For example, you can turn machine_metrics above into a hypertable by running:

SELECT create_hypertable('machine_metrics','time'); ​

Learn More

To learn more about Timescale and hypertables, check out our getting started guide.

On this page