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Tiger MCP vs. a Generic Postgres MCP Server
Time-Series Analysis and Forecasting With Python Why Consider Using PostgreSQL for Time-Series Data?Time-Series Analysis in RWhat Is Temporal Data?Understanding Autoregressive Time-Series ModelingAlternatives to TimescaleStationary Time-Series AnalysisWhat Are Open-Source Time-Series Databases—Understanding Your OptionsIs Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleUnderstanding Database Workloads: Variable, Bursty, and Uniform PatternsThe Best Time-Series Databases Compared (2026)Time Series Forecasting with PostgreSQL: Methods, SQL Examples, and When to Use PythonTime Series Anomaly Detection: Methods, SQL, and Real-Time ImplementationAWS Timestream Alternatives: Your Migration Options After LiveAnalyticsTime-Series Database: What It Is, How It Works, and When You Need OneWhat 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 PythonCreating a Fast Time-Series Graph With Postgres Materialized Views
GPU Cluster Monitoring: The Database Behind AI Infrastructure TelemetryWhat Is DCIM Software, and What Database Does It Use?Data Center Power Monitoring: The Database Layer Behind PUE, PDU, and Sustainability MetricsPrometheus Long-Term Storage: Thanos, Mimir, VictoriaMetrics, and PostgreSQL Compared
Understanding PostgreSQLUnderstanding SQL Aggregate FunctionsUnderstanding the rank() and dense_rank() Functions in PostgreSQLUnderstanding percentile_cont() and percentile_disc() in PostgreSQLStructured vs. Semi-Structured vs. Unstructured Data in PostgreSQLOptimizing Your Database: A Deep Dive into PostgreSQL Data TypesPostgres Cheat SheetHow to Address ‘Error: Could Not Resize Shared Memory Segment’ Understanding the Postgres extract() FunctionUnderstanding FROM in PostgreSQL (With Examples)How to Install PostgreSQL on LinuxHow to Install PostgreSQL on MacOSUnderstanding FILTER in PostgreSQL (With Examples)Understanding HAVING in PostgreSQL (With Examples)5 Common Connection Errors in PostgreSQL and How to Solve ThemUnderstanding GROUP BY in PostgreSQL (With Examples)How to Fix No Partition of Relation Found for Row in Postgres DatabasesUnderstanding LIMIT in PostgreSQL (With Examples)How to Fix Transaction ID Wraparound ExhaustionUnderstanding ORDER BY in PostgreSQL (With Examples)Understanding PostgreSQL FunctionsUnderstanding WINDOW in PostgreSQL (With Examples)Understanding PostgreSQL WITHIN GROUPPostgreSQL Mathematical Functions: Enhancing Coding EfficiencyUnderstanding DISTINCT in PostgreSQL (With Examples)Understanding WHERE in PostgreSQL (With Examples)Understanding OFFSET in PostgreSQL (With Examples)Understanding the Postgres string_agg FunctionUnderstanding PostgreSQL SELECTWhat Characters Are Allowed in PostgreSQL Strings?What Is Data Transformation, and Why Is It Important?What Is Data Compression and How Does It Work?Using PostgreSQL UPDATE With JOINUnderstanding PostgreSQL User-Defined FunctionsUnderstanding Foreign Keys in PostgreSQLData Processing With PostgreSQL Window FunctionsUnderstanding PostgreSQL Date and Time FunctionsWhat Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?PostgreSQL Join Type TheoryA Guide to PostgreSQL ViewsStrategies for Improving Postgres JOIN PerformanceUnderstanding PostgreSQL's COALESCE FunctionUnderstanding PostgreSQL Array FunctionsUnderstanding PostgreSQL Conditional FunctionsUsing PostgreSQL String Functions for Improved Data AnalysisPostgreSQL vs. Cassandra: The Decision Framework for Time-Series and Write-Heavy WorkloadsPostgreSQL Joins : A SummaryWhat Is a PostgreSQL Cross Join?Data Partitioning: What It Is and Why It MattersSelf-Hosted or Cloud Database? A Countryside Reflection on Infrastructure ChoicesUnderstanding ACID Compliance
How to Monitor and Optimize PostgreSQL Index PerformanceGuide to PostgreSQL PerformancePostgreSQL Performance Tuning: Designing and Implementing Your Database SchemaPostgreSQL Performance Tuning: Key ParametersPostgreSQL Performance Tuning: Optimizing Database IndexesHow to Reduce Bloat in Large PostgreSQL TablesDetermining the Optimal Postgres Partition SizeNavigating Growing PostgreSQL Tables With Partitioning (and More)When to Consider Postgres PartitioningAn 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 EXPLAINBest Practices for (Time-)Series Metadata Tables What Is a PostgreSQL Temporary View?A PostgreSQL Database Replication GuideUnderstanding PostgreSQL TablespacesGuide to Postgres Data ManagementA Guide to Data Analysis on PostgreSQLHow to Query JSONB in PostgreSQLHow to Compute Standard Deviation With PostgreSQLPostgreSQL Performance Tuning: How to Size Your DatabaseSQL/JSON Data Model and JSON in SQL: A PostgreSQL PerspectiveTop PostgreSQL Drivers for PythonPg_partman vs. Hypertables for Postgres PartitioningGuide to PostgreSQL Database DesignHow PostgreSQL Data Aggregation WorksBuilding a Scalable DatabaseA Guide to pg_restore (and pg_restore Example)How to Index JSONB Columns in PostgreSQLWhat Is Audit Logging and How to Enable It in PostgreSQLOptimizing Array Queries With GIN Indexes in PostgreSQLGuide to PostgreSQL SecurityHow to Query JSON Metadata in PostgreSQLRecursive Query in SQL: What It Is, and How to Write OneHow to Use PostgreSQL for Data TransformationHandling Large Objects in PostgresA Guide to Scaling PostgreSQLAWS Timestream for InfluxDB Alternative: When You Need to Look FurtherHow to Migrate from AWS Timestream to PostgreSQL: A Technical GuideHow to Choose a Database: A Decision Framework for Modern ApplicationsHow to Use Psycopg2: The PostgreSQL Adapter for Python
Best Practices for Scaling PostgreSQLHow to Store Video in PostgreSQL Using BYTEABest Practices for Postgres PerformanceHow to Design Your PostgreSQL Database: Two Schema ExamplesBest Practices for PostgreSQL Database OperationsHow to Manage Your Data With Data Retention PoliciesBest Practices for PostgreSQL Data AnalysisBest Practices for Postgres Database ReplicationBest Practices for Postgres Data ManagementBest Practices for PostgreSQL AggregationHow to Use a Common Table Expression (CTE) in SQLHow to Use PostgreSQL for Data NormalizationTesting Postgres Ingest: INSERT vs. Batch INSERT vs. COPYBest Practices for Postgres SecurityHow to Handle High-Cardinality Data in PostgreSQLPostgreSQL Compression: Every Option, When To Use Each, and What To Expect
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-osspTurning PostgreSQL Into a Vector Database With pgvector
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
Fleet Telemetry Database: How to Store and Query Vehicle Sensor Data at ScaleUnderstanding IoT (Internet of Things)A Beginner’s Guide to IIoT and Industry 4.0DERMS Database: Solving the Single Source of DER Data ProblemForecasting the Physical World: Foundation Models for Predictive MaintenanceBuilding Energy Management System: The Data Layer Behind Energy Monitoring, Sub-Metering, and M&VSmart Building Analytics: Turning BMS Telemetry Into Occupancy, Energy, and HVAC InsightEV Charging Load ManagementBuilding Management System Database: Architecture for BACnet, Modbus, and Smart Building Sensor Data at ScaleMeter Data Management: Why AMI Interval Data Breaks Legacy MDM SystemsSmart Grid Data Platform: Architecting for SCADA, AMI, PMU, and DERMS Data at ScaleDigital Twin Architecture: The Database and Data Model Behind a Digital TwinCAN Bus Data Logger: Decoding DBC and J1939 Signals into PostgresPredictive Maintenance Database Architecture: Storing and Querying Sensor Data for Failure PredictionPhysical AI Telemetry: Database Architecture for Autonomous-Vehicle FleetsPlant Historian: What It Captures and Where the Analytics Layer BeginsManufacturing Analytics Database: Architecture for Real-Time Production DataRobot Fleet Telemetry: Database Architecture for AMR, Industrial Robot, and Service Robot DataData Center Monitoring: What Database Stores Your Telemetry?EV Charging Management System: Architecture, OCPP Data, and the Right DatabaseIIoT Database Requirements: Six Things Your Database Must DoWater Utilities Database: How to Store and Query SCADA, AMI, and Quality Data at ScaleWhat Is an Edge Database? On-Device Storage, Sync Patterns, and Choosing the Right StackData Historian vs. Time-Series Database: How to Choose and When to SwitchWhat Is a Data Historian?The Best Databases for IoT in 2026: A Practical ComparisonHow Hopthru Powers Real-Time Transit Analytics From a 1 TB TableStoring IoT Data: 8 Reasons Why You Should Use PostgreSQLHow 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
A 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 LangChainRAG Is More Than Just Vector SearchRefining Vector Search Queries With Time Filters in Pgvector: A TutorialUnderstanding Semantic SearchWhat Is Vector Search? Text-to-SQL: A Developer’s Zero-to-Hero GuideWhen Should You Use Full-Text Search vs. Vector Search?HNSW vs. DiskANNVector Search vs Semantic SearchBuilding AI Agents with Persistent Memory: A Unified Database ApproachNearest Neighbor Indexes: What Are IVFFlat Indexes in Pgvector and How Do They WorkPostgreSQL Hybrid Search Using Pgvector and CohereBuilding an AI Image Gallery With OpenAI CLIP, Claude Sonnet 3.5, and Pgvector
Data Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)How to Choose a Real-Time Analytics DatabaseColumnar Databases vs. Row-Oriented Databases: Which to Choose?What Is the Best Database for Real-Time AnalyticsPostgreSQL as a Real-Time Analytics DatabaseUnderstanding OLTPUnderstanding OLAP: What It Is, How It Differs From OLTP, and Running It on PostgreSQLHow to Choose an OLAP DatabaseHow 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
5 Ways to Monitor Your PostgreSQL DatabaseHow 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 HyperfunctionsIs Postgres Partitioning Really That Hard? An Introduction To HypertablesPostgreSQL Materialized Views and Where to Find ThemTime-Series Downsampling: The Complete Guide to Tiered Data Resolution in SQLContinuous Aggregates: Incremental Materialized Views for Time-Series DataComplete Guide: Migrating from MongoDB to Tiger Data (Step-by-Step)Timescale Tips: Testing Your Chunk Size
Postgres cheat sheet
HomeAlternativesTime-series basicsData Center & Infrastructure TelemetryPostgres basicsPostgres guidesPostgres best practices
Home
Tiger MCP vs. a Generic Postgres MCP Server
Time-Series Analysis and Forecasting With Python Why Consider Using PostgreSQL for Time-Series Data?Time-Series Analysis in RWhat Is Temporal Data?Understanding Autoregressive Time-Series ModelingAlternatives to TimescaleStationary Time-Series AnalysisWhat Are Open-Source Time-Series Databases—Understanding Your OptionsIs Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleUnderstanding Database Workloads: Variable, Bursty, and Uniform PatternsThe Best Time-Series Databases Compared (2026)Time Series Forecasting with PostgreSQL: Methods, SQL Examples, and When to Use PythonTime Series Anomaly Detection: Methods, SQL, and Real-Time ImplementationAWS Timestream Alternatives: Your Migration Options After LiveAnalyticsTime-Series Database: What It Is, How It Works, and When You Need OneWhat 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 PythonCreating a Fast Time-Series Graph With Postgres Materialized Views
GPU Cluster Monitoring: The Database Behind AI Infrastructure TelemetryWhat Is DCIM Software, and What Database Does It Use?Data Center Power Monitoring: The Database Layer Behind PUE, PDU, and Sustainability MetricsPrometheus Long-Term Storage: Thanos, Mimir, VictoriaMetrics, and PostgreSQL Compared
Understanding PostgreSQLUnderstanding SQL Aggregate FunctionsUnderstanding the rank() and dense_rank() Functions in PostgreSQLUnderstanding percentile_cont() and percentile_disc() in PostgreSQLStructured vs. Semi-Structured vs. Unstructured Data in PostgreSQLOptimizing Your Database: A Deep Dive into PostgreSQL Data TypesPostgres Cheat SheetHow to Address ‘Error: Could Not Resize Shared Memory Segment’ Understanding the Postgres extract() FunctionUnderstanding FROM in PostgreSQL (With Examples)How to Install PostgreSQL on LinuxHow to Install PostgreSQL on MacOSUnderstanding FILTER in PostgreSQL (With Examples)Understanding HAVING in PostgreSQL (With Examples)5 Common Connection Errors in PostgreSQL and How to Solve ThemUnderstanding GROUP BY in PostgreSQL (With Examples)How to Fix No Partition of Relation Found for Row in Postgres DatabasesUnderstanding LIMIT in PostgreSQL (With Examples)How to Fix Transaction ID Wraparound ExhaustionUnderstanding ORDER BY in PostgreSQL (With Examples)Understanding PostgreSQL FunctionsUnderstanding WINDOW in PostgreSQL (With Examples)Understanding PostgreSQL WITHIN GROUPPostgreSQL Mathematical Functions: Enhancing Coding EfficiencyUnderstanding DISTINCT in PostgreSQL (With Examples)Understanding WHERE in PostgreSQL (With Examples)Understanding OFFSET in PostgreSQL (With Examples)Understanding the Postgres string_agg FunctionUnderstanding PostgreSQL SELECTWhat Characters Are Allowed in PostgreSQL Strings?What Is Data Transformation, and Why Is It Important?What Is Data Compression and How Does It Work?Using PostgreSQL UPDATE With JOINUnderstanding PostgreSQL User-Defined FunctionsUnderstanding Foreign Keys in PostgreSQLData Processing With PostgreSQL Window FunctionsUnderstanding PostgreSQL Date and Time FunctionsWhat Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?PostgreSQL Join Type TheoryA Guide to PostgreSQL ViewsStrategies for Improving Postgres JOIN PerformanceUnderstanding PostgreSQL's COALESCE FunctionUnderstanding PostgreSQL Array FunctionsUnderstanding PostgreSQL Conditional FunctionsUsing PostgreSQL String Functions for Improved Data AnalysisPostgreSQL vs. Cassandra: The Decision Framework for Time-Series and Write-Heavy WorkloadsPostgreSQL Joins : A SummaryWhat Is a PostgreSQL Cross Join?Data Partitioning: What It Is and Why It MattersSelf-Hosted or Cloud Database? A Countryside Reflection on Infrastructure ChoicesUnderstanding ACID Compliance
How to Monitor and Optimize PostgreSQL Index PerformanceGuide to PostgreSQL PerformancePostgreSQL Performance Tuning: Designing and Implementing Your Database SchemaPostgreSQL Performance Tuning: Key ParametersPostgreSQL Performance Tuning: Optimizing Database IndexesHow to Reduce Bloat in Large PostgreSQL TablesDetermining the Optimal Postgres Partition SizeNavigating Growing PostgreSQL Tables With Partitioning (and More)When to Consider Postgres PartitioningAn 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 EXPLAINBest Practices for (Time-)Series Metadata Tables What Is a PostgreSQL Temporary View?A PostgreSQL Database Replication GuideUnderstanding PostgreSQL TablespacesGuide to Postgres Data ManagementA Guide to Data Analysis on PostgreSQLHow to Query JSONB in PostgreSQLHow to Compute Standard Deviation With PostgreSQLPostgreSQL Performance Tuning: How to Size Your DatabaseSQL/JSON Data Model and JSON in SQL: A PostgreSQL PerspectiveTop PostgreSQL Drivers for PythonPg_partman vs. Hypertables for Postgres PartitioningGuide to PostgreSQL Database DesignHow PostgreSQL Data Aggregation WorksBuilding a Scalable DatabaseA Guide to pg_restore (and pg_restore Example)How to Index JSONB Columns in PostgreSQLWhat Is Audit Logging and How to Enable It in PostgreSQLOptimizing Array Queries With GIN Indexes in PostgreSQLGuide to PostgreSQL SecurityHow to Query JSON Metadata in PostgreSQLRecursive Query in SQL: What It Is, and How to Write OneHow to Use PostgreSQL for Data TransformationHandling Large Objects in PostgresA Guide to Scaling PostgreSQLAWS Timestream for InfluxDB Alternative: When You Need to Look FurtherHow to Migrate from AWS Timestream to PostgreSQL: A Technical GuideHow to Choose a Database: A Decision Framework for Modern ApplicationsHow to Use Psycopg2: The PostgreSQL Adapter for Python
Best Practices for Scaling PostgreSQLHow to Store Video in PostgreSQL Using BYTEABest Practices for Postgres PerformanceHow to Design Your PostgreSQL Database: Two Schema ExamplesBest Practices for PostgreSQL Database OperationsHow to Manage Your Data With Data Retention PoliciesBest Practices for PostgreSQL Data AnalysisBest Practices for Postgres Database ReplicationBest Practices for Postgres Data ManagementBest Practices for PostgreSQL AggregationHow to Use a Common Table Expression (CTE) in SQLHow to Use PostgreSQL for Data NormalizationTesting Postgres Ingest: INSERT vs. Batch INSERT vs. COPYBest Practices for Postgres SecurityHow to Handle High-Cardinality Data in PostgreSQLPostgreSQL Compression: Every Option, When To Use Each, and What To Expect
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-osspTurning PostgreSQL Into a Vector Database With pgvector
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
Fleet Telemetry Database: How to Store and Query Vehicle Sensor Data at ScaleUnderstanding IoT (Internet of Things)A Beginner’s Guide to IIoT and Industry 4.0DERMS Database: Solving the Single Source of DER Data ProblemForecasting the Physical World: Foundation Models for Predictive MaintenanceBuilding Energy Management System: The Data Layer Behind Energy Monitoring, Sub-Metering, and M&VSmart Building Analytics: Turning BMS Telemetry Into Occupancy, Energy, and HVAC InsightEV Charging Load ManagementBuilding Management System Database: Architecture for BACnet, Modbus, and Smart Building Sensor Data at ScaleMeter Data Management: Why AMI Interval Data Breaks Legacy MDM SystemsSmart Grid Data Platform: Architecting for SCADA, AMI, PMU, and DERMS Data at ScaleDigital Twin Architecture: The Database and Data Model Behind a Digital TwinCAN Bus Data Logger: Decoding DBC and J1939 Signals into PostgresPredictive Maintenance Database Architecture: Storing and Querying Sensor Data for Failure PredictionPhysical AI Telemetry: Database Architecture for Autonomous-Vehicle FleetsPlant Historian: What It Captures and Where the Analytics Layer BeginsManufacturing Analytics Database: Architecture for Real-Time Production DataRobot Fleet Telemetry: Database Architecture for AMR, Industrial Robot, and Service Robot DataData Center Monitoring: What Database Stores Your Telemetry?EV Charging Management System: Architecture, OCPP Data, and the Right DatabaseIIoT Database Requirements: Six Things Your Database Must DoWater Utilities Database: How to Store and Query SCADA, AMI, and Quality Data at ScaleWhat Is an Edge Database? On-Device Storage, Sync Patterns, and Choosing the Right StackData Historian vs. Time-Series Database: How to Choose and When to SwitchWhat Is a Data Historian?The Best Databases for IoT in 2026: A Practical ComparisonHow Hopthru Powers Real-Time Transit Analytics From a 1 TB TableStoring IoT Data: 8 Reasons Why You Should Use PostgreSQLHow 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
A 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 LangChainRAG Is More Than Just Vector SearchRefining Vector Search Queries With Time Filters in Pgvector: A TutorialUnderstanding Semantic SearchWhat Is Vector Search? Text-to-SQL: A Developer’s Zero-to-Hero GuideWhen Should You Use Full-Text Search vs. Vector Search?HNSW vs. DiskANNVector Search vs Semantic SearchBuilding AI Agents with Persistent Memory: A Unified Database ApproachNearest Neighbor Indexes: What Are IVFFlat Indexes in Pgvector and How Do They WorkPostgreSQL Hybrid Search Using Pgvector and CohereBuilding an AI Image Gallery With OpenAI CLIP, Claude Sonnet 3.5, and Pgvector
Data Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)How to Choose a Real-Time Analytics DatabaseColumnar Databases vs. Row-Oriented Databases: Which to Choose?What Is the Best Database for Real-Time AnalyticsPostgreSQL as a Real-Time Analytics DatabaseUnderstanding OLTPUnderstanding OLAP: What It Is, How It Differs From OLTP, and Running It on PostgreSQLHow to Choose an OLAP DatabaseHow 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
5 Ways to Monitor Your PostgreSQL DatabaseHow 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 HyperfunctionsIs Postgres Partitioning Really That Hard? An Introduction To HypertablesPostgreSQL Materialized Views and Where to Find ThemTime-Series Downsampling: The Complete Guide to Tiered Data Resolution in SQLContinuous Aggregates: Incremental Materialized Views for Time-Series DataComplete Guide: Migrating from MongoDB to Tiger Data (Step-by-Step)Timescale Tips: Testing Your Chunk Size
Postgres cheat sheet
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All rights reserved.

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By Tiger Data team

Published at Mar 15, 2024

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    Best Practices for Postgres Security

    A vault with several engraved elephants on its door.
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    By Tiger Data team

    Published at Mar 15, 2024

    Written by Juan José Gouvêa

    As a PostgreSQL user, you will have to dig into many topics to get the most from your PostgreSQL database, from replication to schemas and performance. None, however, is as critical as Postgres security. Ensuring the security of databases is paramount in protecting your organization’s data assets. This means not only safeguarding data integrity and thwarting malicious threats but also adhering to legal obligations related to access permissions and privacy regulations.

    In this article, we’ll look at some best practices for securing your PostgreSQL database, mitigating risks, and ensuring the safety of your data from unauthorized access and potential breaches.

    PostgreSQL Access Control

    To ensure that only authorized processes and remote connections have access to your PostgreSQL database—providing a foundational layer of security for all your PostgreSQL deployments—you need to secure access via Unix Domain Sockets (UDS) and TCP/IP. Both require careful configuration of socket parameters and firewall rules.

    Unix Domain Sockets

    Unix Domain Sockets (UDS) provide a file-based access control system, managing permissions between processes on the same host. They facilitate data exchange between processes, establishing a secure and efficient method for local inter-process communication.

    PostgreSQL UDS configuration: PostgreSQL allows configuration of Unix Domain Sockets through parameters such as unix_socket_directories, unix_socket_group, and unix_socket_permissions. These parameters enable precise control over the location, access group, and permissions of the socket files, enhancing security for database connections.

    • unix_socket_directories: Defines the directories where Unix-domain sockets for client connections will be placed.

    • unix_socket_group: Sets the owning group of the Unix-domain sockets.

    • unix_socket_permissions: Specifies the access permissions for the Unix-domain sockets.

    TCP/IP

    TCP/IP protocol is pivotal for managing remote database connections, defining how data is transmitted over networks. It includes specifying port access to ensure secure and controlled access to PostgreSQL databases.

    Firewall configuration: By default, PostgreSQL listens on port 5432 for incoming connections. Configuring a firewall to restrict access to this port is essential for preventing unauthorized access. Only known and trusted IP addresses should be allowed to connect, significantly reducing the risk of external attacks.

    Example firewall rule:

    sudo iptables -A INPUT -p tcp --dport 5432 -s trusted_ip_address -j ACCEPT sudo iptables -A INPUT -p tcp --dport 5432 -j DROP

    This setup allows connections to port 5432 from a trusted IP address while dropping requests from unknown sources.

    PostgreSQL User Roles and Groups

    Creating users

    Creating users in PostgreSQL is fundamental for database access management. Users are distinct from operating system users, having global scope across a database cluster rather than being restricted to individual databases. The primary SQL command for this operation is CREATE USER, which allows specifying the username and optionally, a password for the user. The process is straightforward:

    CREATE USER username WITH PASSWORD 'password';

    This command creates a new database user with login capabilities, setting the groundwork for defining access levels and permissions specific to database operations.

    Creating roles

    Roles in PostgreSQL serve as a versatile mechanism for managing database permissions. They can represent individual users, groups of users, or a set of permissions. Essentially, roles allow for the assignment of access rights and responsibilities within the database system. Creating a role is done using the CREATE ROLE command and it can include attributes such as login permissions, database creation rights, and role management capabilities:

    CREATE ROLE role_name WITH LOGIN CREATEDB;

    This example demonstrates the creation of a role with the ability to log in and create databases. Roles streamline the management of permissions, making it easier to enforce security policies through a centralized role-based access control system.

    Row-level permission

    PostgreSQL's Row-Level Security (RLS) feature adds a finer granularity of access control, allowing administrators to define policies that restrict access to specific rows within a table. This is particularly useful in multi-tenant environments where users should only see their data. To enable RLS, you use the ALTER TABLE command:

    ALTER TABLE table_name ENABLE ROW LEVEL SECURITY;

    Following this, policies can be defined to specify the conditions under which rows are visible or modifiable by a particular role or user, using the CREATE POLICY command. This enables precise control over who can access or modify individual rows within a table, enhancing the security and privacy of the data stored in PostgreSQL databases.

    Implementing comprehensive user roles and groups, alongside row-level permissions, is pivotal in securing PostgreSQL databases. These mechanisms together provide a robust framework for managing access control, ensuring that users have appropriate permissions tailored to their roles within an organization.

    PostgreSQL Encryption

    SSL encryption

    SSL (Secure Sockets Layer) encryption is a fundamental security feature for safeguarding data in transit, providing end-to-end security to prevent man-in-the-middle attacks. While PostgreSQL does not natively implement SSL, it supports SSL connections to encrypt data between clients and servers. This ensures that sensitive information remains secure during transmission. A full tutorial on configuring SSL for PostgreSQL would be too extensive for this blog post, but an overview involves generating SSL certificates, configuring the PostgreSQL server for SSL, and enforcing SSL connections for clients.

    Database encryption with pgcrypto

    The pgcrypto extension adds robust encryption capabilities directly within PostgreSQL, allowing for the encryption of individual fields, entire tables, or data transactions. This is particularly useful for sensitive data that requires encryption at rest, such as healthcare records or personal identification information.

    Basic example of using pgcrypto: To encrypt data without specifying a key explicitly, you can use the pgp_sym_encrypt function. Here's a simple example that encrypts a text string:

    SELECT pgp_sym_encrypt('Sensitive data here', 'encryption_key_here') AS encrypted_data;

    Decrypting the data can be done with the corresponding pgp_sym_decrypt function:

    SELECT pgp_sym_decrypt(encrypted_data, 'encryption_key_here') AS original_data FROM (SELECT pgp_sym_encrypt('Sensitive data here', 'encryption_key_here') AS encrypted_data) AS subquery;

    These examples highlight PostgreSQL's flexibility in managing data security, offering tools for both securing data in transit with SSL and protecting data at rest using pgcrypto. Implementing these security measures can significantly enhance the overall security posture of PostgreSQL databases, ensuring sensitive information is adequately protected against unauthorized access or exposure.

    PostgreSQL Security With Timescale

    PostgreSQL security support

    Timescale enhances PostgreSQL by seamlessly integrating with it, inheriting its robust security framework while introducing its own advanced features. This integration ensures that users benefit from the foundational security measures PostgreSQL is known for, including authentication, authorization, and data encryption capabilities.

    Using pgcrypto with Timescale

    The pgcrypto extension is a key component of PostgreSQL's security, offering cryptographic functions such as encryption, decryption, and hashing directly within the database. Timescale fully supports pgcrypto, enabling users to secure time-series data efficiently. Implementing pgcrypto with Timescale involves simple steps that greatly enhance data security.

    How to use pgcrypto with Timescale

    After ensuring Timescale and PostgreSQL are properly installed, you can activate pgcrypto by executing the command:

    CREATE EXTENSION IF NOT EXISTS pgcrypto;

    This allows the use of various cryptographic operations within your Timescale database environment. For example, to encrypt sensor readings in a hypertable, you could use the following approach:

    -- Create a table for sensor readings CREATE TABLE sensor_readings ( time TIMESTAMPTZ NOT NULL, device_id BIGINT, reading BYTEA ); -- Convert the table into a hypertable SELECT create_hypertable('sensor_readings', 'time'); -- Insert an encrypted reading INSERT INTO sensor_readings (time, device_id, reading) VALUES (NOW(), 1, pgp_sym_encrypt('20.5', 'supersecretkey'));

    To decrypt the data for use, you would use:

    SELECT time, device_id, pgp_sym_decrypt(reading, 'supersecretkey') AS reading FROM sensor_readings WHERE device_id = 1;

    These examples showcase the straightforward application of pgcrypto functions to encrypt and decrypt data, ensuring that sensitive information remains secure even when stored in backups or dumps.

    Timescale Security Features

    Timescale goes beyond the baseline security provided by PostgreSQL to offer several advanced features that ensure comprehensive protection for your data:

    • Data encryption: Data in Timescale is always encrypted, both in transit and at rest, using industry-standard algorithms. This includes active databases and backups, with encryption keys managed securely by AWS Key Management Service (KMS).

    • Networking security: Access to Timescale services is strictly over TLS-encrypted connections, with no option for unencrypted plaintext connections. This applies to all customer interactions with Timescale services.

    • Maintenance access protocol: Timescale employs a rigorous protocol for maintenance access, with all operator actions audit logged. This ensures that any necessary troubleshooting is conducted securely and transparently.

    • Privacy compliance: Timescale is committed to customer data privacy, complying with regulations such as GDPR. This commitment is reflected in Timescale's privacy policy and terms of service, ensuring that customer data is handled with the utmost care and respect.

    • SOC 2 Type 2 compliance: Timescale is SOC 2 Type 2 compliant, and our SOC 2 report is available for all the customers in our Enterprise Tier.

    • VPC peering: With Virtual Private Cloud (VPC) peering, you can replicate (as much as possible) the isolation of a self-hosted deployment and access your cloud data without the risk of having your services exposed to the public internet.

    Check out our Docs or the Security at Timescale page to learn more.

    Conclusion

    In this article, we've explored essential best practices for securing a PostgreSQL database. We delved into access control, user roles and groups, and encryption techniques, providing a foundation to safeguard your data effectively. By understanding how to configure Unix Domain Sockets, TCP/IP protocols, implement row-level security, and utilize pgcrypto for encryption, you can mitigate risks and enhance the overall security posture of your PostgreSQL databases.

    Timescale further extends PostgreSQL's robust security framework, offering advanced features like seamless integration of pgcrypto for cryptographic functions and comprehensive data protection strategies that encompass both data in transit and at rest. These enhancements are crucial for managing time-series data securely, ensuring that your sensitive information is protected against unauthorized access or exposure.

    If you want to maximize your time-series data with top-of-the-line security, try Timescale today.