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
Is Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleUnderstanding Database Workloads: Variable, Bursty, and Uniform PatternsTime-Series Analysis and Forecasting With Python What Is Temporal Data?What Are Open-Source Time-Series Databases—Understanding Your OptionsAWS Time-Series Database: Understanding Your OptionsStationary Time-Series AnalysisThe Best Time-Series Databases ComparedAlternatives to TimescaleWhy Consider Using PostgreSQL for Time-Series Data?Time-Series Analysis in RWhat Is a Time Series and How Is It Used?How to Work With Time Series in Python?Tools for Working With Time-Series Analysis in PythonGuide to Time-Series Analysis in PythonUnderstanding Autoregressive Time-Series ModelingCreating a Fast Time-Series Graph With Postgres Materialized Views
Optimizing Your Database: A Deep Dive into PostgreSQL Data TypesHow to Install PostgreSQL on LinuxUnderstanding FROM in PostgreSQL (With Examples)How to Address ‘Error: Could Not Resize Shared Memory Segment’ Understanding FILTER in PostgreSQL (With Examples)How to Install PostgreSQL on MacOS5 Common Connection Errors in PostgreSQL and How to Solve ThemUnderstanding HAVING in PostgreSQL (With Examples)How to Fix No Partition of Relation Found for Row in Postgres DatabasesHow to Fix Transaction ID Wraparound ExhaustionUnderstanding LIMIT in PostgreSQL (With Examples)Understanding PostgreSQL FunctionsUnderstanding ORDER BY in PostgreSQL (With Examples)PostgreSQL Mathematical Functions: Enhancing Coding EfficiencyUnderstanding PostgreSQL WITHIN GROUPUnderstanding WINDOW in PostgreSQL (With Examples)Using PostgreSQL String Functions for Improved Data AnalysisPostgreSQL Joins : A SummaryUnderstanding PostgreSQL Conditional FunctionsStructured vs. Semi-Structured vs. Unstructured Data in PostgreSQLUnderstanding DISTINCT in PostgreSQL (With Examples)What Is a PostgreSQL Cross Join?Understanding percentile_cont() and percentile_disc() in PostgreSQLUnderstanding GROUP BY in PostgreSQL (With Examples)Data Processing With PostgreSQL Window FunctionsUnderstanding WHERE in PostgreSQL (With Examples)Data Partitioning: What It Is and Why It MattersUnderstanding PostgreSQL Array FunctionsUnderstanding ACID Compliance Understanding PostgreSQL's COALESCE FunctionUnderstanding the rank() and dense_rank() Functions in PostgreSQLUnderstanding PostgreSQLUnderstanding OFFSET in PostgreSQL (With Examples)Understanding PostgreSQL Date and Time FunctionsUnderstanding the Postgres string_agg FunctionWhat Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?Strategies for Improving Postgres JOIN PerformancePostgreSQL Join Type TheoryA Guide to PostgreSQL ViewsUnderstanding Foreign Keys in PostgreSQLUnderstanding PostgreSQL User-Defined FunctionsUnderstanding SQL Aggregate FunctionsUsing PostgreSQL UPDATE With JOINWhat Is Data Compression and How Does It Work?What Is Data Transformation, and Why Is It Important?What Characters Are Allowed in PostgreSQL Strings?Understanding PostgreSQL SELECTSelf-Hosted or Cloud Database? A Countryside Reflection on Infrastructure ChoicesUnderstanding the Postgres extract() Function
How to Choose a Database: A Decision Framework for Modern ApplicationsHandling Large Objects in PostgresDetermining the Optimal Postgres Partition SizeNavigating Growing PostgreSQL Tables With Partitioning (and More)SQL/JSON Data Model and JSON in SQL: A PostgreSQL PerspectiveHow to Use PostgreSQL for Data TransformationPostgreSQL Performance Tuning: Designing and Implementing Your Database SchemaGuide to PostgreSQL PerformancePostgreSQL Performance Tuning: Key ParametersPostgreSQL Performance Tuning: Optimizing Database IndexesHow to Reduce Bloat in Large PostgreSQL TablesAn Intro to Data Modeling on PostgreSQLDesigning Your Database Schema: Wide vs. Narrow Postgres TablesGuide to PostgreSQL Database OperationsBest Practices for Time-Series Data Modeling: Single or Multiple Partitioned Table(s) a.k.a. Hypertables Explaining PostgreSQL EXPLAINWhat Is a PostgreSQL Temporary View?PostgreSQL Performance Tuning: How to Size Your DatabaseBest Practices for (Time-)Series Metadata Tables A PostgreSQL Database Replication GuideHow to Compute Standard Deviation With PostgreSQLA Guide to Data Analysis on PostgreSQLA Guide to Scaling PostgreSQLRecursive Query in SQL: What It Is, and How to Write OneHow to Query JSON Metadata in PostgreSQLHow to Query JSONB in PostgreSQLGuide to PostgreSQL SecurityOptimizing Array Queries With GIN Indexes in PostgreSQLPg_partman vs. Hypertables for Postgres PartitioningTop PostgreSQL Drivers for PythonUnderstanding PostgreSQL TablespacesWhat Is Audit Logging and How to Enable It in PostgreSQLWhen to Consider Postgres PartitioningGuide to Postgres Data ManagementHow to Index JSONB Columns in PostgreSQLHow to Monitor and Optimize PostgreSQL Index PerformanceA Guide to pg_restore (and pg_restore Example)How PostgreSQL Data Aggregation WorksHow to Use Psycopg2: The PostgreSQL Adapter for PythonBuilding a Scalable DatabaseGuide to PostgreSQL Database Design
Best Practices for Postgres Data ManagementHow to Store Video in PostgreSQL Using BYTEABest Practices for Postgres PerformanceHow to Design Your PostgreSQL Database: Two Schema ExamplesBest Practices for Scaling PostgreSQLHow to Handle High-Cardinality Data in PostgreSQLBest Practices for PostgreSQL AggregationBest Practices for Postgres Database ReplicationHow to Use a Common Table Expression (CTE) in SQLBest Practices for Postgres SecurityBest Practices for PostgreSQL Database OperationsBest Practices for PostgreSQL Data AnalysisTesting Postgres Ingest: INSERT vs. Batch INSERT vs. COPYHow to Manage Your Data With Data Retention PoliciesHow to Use PostgreSQL for Data Normalization
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: Turning PostgreSQL Into a Vector Database With pgvectorPostgreSQL Extensions: Intro to uuid-ossp
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
Moving Past Legacy Systems: Data Historian vs. Time-Series DatabaseHow Hopthru Powers Real-Time Transit Analytics From a 1 TB TableUnderstanding IoT (Internet of Things)A Beginner’s Guide to IIoT and Industry 4.0Storing 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 % CompressionWhy You Should Use PostgreSQL for Industrial IoT Data Migrating a Low-Code IoT Platform Storing 20M Records/DayHow 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
Building AI Agents with Persistent Memory: A Unified Database ApproachA Brief History of AI: How Did We Get Here, and What's Next?A 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 DiskANNA Guide to Cosine SimilarityStreaming DiskANN: How We Made PostgreSQL as Fast as Pinecone for Vector DataImplementing 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 PgvectorRAG Is More Than Just Vector SearchImplementing Filtered Semantic Search Using Pgvector and JavaScriptWhen Should You Use Full-Text Search vs. Vector Search?HNSW vs. DiskANNPostgreSQL Hybrid Search Using Pgvector and CohereRefining Vector Search Queries With Time Filters in Pgvector: A TutorialUnderstanding Semantic SearchWhat Is Vector Search? Vector Search vs Semantic SearchText-to-SQL: A Developer’s Zero-to-Hero GuideNearest Neighbor Indexes: What Are IVFFlat Indexes in Pgvector and How Do They WorkBuilding an AI Image Gallery With OpenAI CLIP, Claude Sonnet 3.5, and Pgvector
How to Choose a Real-Time Analytics DatabaseData Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)Understanding OLTPOLAP Workloads on PostgreSQL: A GuideColumnar Databases vs. Row-Oriented Databases: Which to Choose?How to Choose an OLAP DatabasePostgreSQL as a Real-Time Analytics DatabaseWhat Is the Best Database for Real-Time AnalyticsHow to Build an IoT Pipeline for Real-Time Analytics in PostgreSQL
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
5 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)What InfluxDB Got WrongTimescaleDB vs. InfluxDB: Purpose Built Differently for Time-Series Data
Is Postgres Partitioning Really That Hard? An Introduction To HypertablesComplete Guide: Migrating from MongoDB to Tiger Data (Step-by-Step)How to Migrate Your Data to Timescale (3 Ways)Postgres TOAST vs. Timescale CompressionBuilding Python Apps With PostgreSQL: A Developer's GuideData Visualization in PostgreSQL With Apache SupersetMore Time-Series Data Analysis, Fewer Lines of Code: Meet HyperfunctionsPostgreSQL Materialized Views and Where to Find Them5 Ways to Monitor Your PostgreSQL DatabaseTimescale Tips: Testing Your Chunk Size
Postgres cheat sheet
HomeTime series basicsPostgres basicsPostgres guidesPostgres best practicesPostgres extensionsBenchmarks
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
TigerData logo

Products

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

Learn

Documentation Blog 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

2026 (c) Timescale, Inc., d/b/a Tiger Data. All rights reserved.

Privacy preferences
LegalPrivacySitemap

Published at Mar 6, 2024

Spatial Database

PostgreSQL Extensions: pgRouting

pgRouting is an extension of PostgreSQL, which turns your database into a spatial database for geographic services. It provides geospatial routing functionality, including shortest path search and driving distance calculations, making it a valuable tool for developers working with location-based services and logistics.

Installing pgRouting

Before you can use pgRouting, you need to install it. Here are the steps to install pgRouting on your PostgreSQL database:

1. Ensure that you have PostgreSQL and PostGIS installed.

2. Download the pgRouting package from the official website or use a package manager like apt or yum.

3. Install the package using the package manager or by building from source.

4. Once installed, you can enable pgRouting in your database using the following SQL command:

CREATE EXTENSION pgrouting;

Using pgRouting

pgRouting provides a variety of functions for routing and network analysis. Here are some examples:

  • pgr_dijkstra(): this function implements the Dijkstra algorithm for finding the shortest path between two nodes.

  • pgr_drivingDistance(): this function calculates the driving distance from a start node to all other nodes within a specified cost.

To use these functions, you need to have a table in your database that represents a graph. Each row in the table represents an edge in the graph, and you need at least the following columns: id, source, target, cost.

Time-Series Use Cases for pgRouting

pgRouting can be used in a variety of time-series use cases. For example, it can be used to analyze traffic data over time, helping to identify patterns and trends in congestion and travel times. It can also be used in logistics to optimize delivery routes based on historical data.

Using pgRouting with Timescale and time-series data

If you're using Timescale, an open-source time-series database built on PostgreSQL, you can use pgRouting to analyze your time-series data in a spatial context. For example, you could use this extension to analyze the movement of a fleet of vehicles over time, identifying patterns and optimizing routes based on this data.

On Timescale, you can find available extensions by going to Operations > Extensions from your service overview, which will also give you installation instructions.

Here is a general process:

1. Set Up Your Database: You'll need to install both pgRouting and TimescaleDB extensions on your PostgreSQL database, as mentioned above. Once installed, you can create a hypertable in TimescaleDB for storing your time-series data.

2. Ingest Time-Series Data: Then, you need to ingest your time-series data into the hypertable. This could be GPS points of a vehicle over time, sensor data from IoT devices, etc. Remember to include timestamps, as they are crucial for time-series data.

3. Prepare Your Geospatial Data: pgRouting works with geospatial data. So, you will need a separate table that contains your routing information. This could be a network of roads, paths, or any other kind of routes represented as a graph. You can use PostGIS extension (which is a prerequisite for pgRouting) for handling this geospatial data.

4. Perform Spatial-Temporal Queries and Analyses: Now that you have both time-series data in a TimescaleDB hypertable and geospatial data in a regular PostgreSQL (or PostGIS) table, you can start performing complex queries and analyses. For example, you could use the pgRouting functions to calculate the shortest path between two points at a certain time.

Remember, the power of using pgRouting with TimescaleDB lies in the ability to analyze how spatial relationships (like routes or distances) change over time. This can provide valuable insights for various applications, like traffic management, fleet tracking, or urban planning.

For a step further in visualizing your data analysis, learn how to create and use a time-series plot.

On this page