March 2026 Summaries

Todd J. Green's blog post discusses the development and implementation of a native BM25 search engine for Postgres, known as pg_textsearch, which is now available to Tiger Cloud customers and as open-source software. Unlike previous solutions that required additional systems like Elasticsearch for full-text search, pg_textsearch integrates BM25 ranking directly into Postgres, improving search performance and ranking quality. This is achieved through features like Block-Max WAND optimization for efficient top-k queries, SIMD-accelerated decoding for reduced index size, and a parallel index build process. The extension is designed to overcome limitations of Postgres's built-in ts_rank, such as lack of inverse document frequency and term frequency saturation, by implementing a robust architecture that includes a hybrid memtable and segment design inspired by LSM-trees. Benchmark tests on the MS-MARCO dataset demonstrate significant speed and efficiency improvements over existing solutions like ParadeDB, especially for shorter queries. Despite these advancements, the current version of pg_textsearch has limitations, including lack of support for phrase queries and fuzzy matching, with future updates planned to address these areas and enhance overall functionality.

Matty Stratton's blog post discusses the limitations of using peak throughput benchmarks to evaluate PostgreSQL performance, highlighting that such tests often fail to capture the challenges of sustained workload conditions in production environments. While peak throughput benchmarks measure the database's performance under ideal conditions for a short duration, they do not account for the long-term effects of continuous data growth and maintenance processes like autovacuum, which can lead to performance degradation over time. Stratton argues that the key metric to focus on is the sustained throughput ceiling, which reflects the database's ability to handle ongoing data ingestion and maintenance processes indefinitely. This ceiling is invariably lower than peak throughput and decreases as data volume increases, making it crucial for teams to consider this factor during capacity planning and benchmarking to avoid issues in production. He suggests that better benchmarking practices, such as running longer load tests with pre-populated data and monitoring maintenance activities, can provide a more accurate picture of a system's long-term performance capabilities.

In high-volume PostgreSQL environments, using wildcard queries such as SELECT * can significantly hinder performance by increasing unnecessary data movement and processing. This inefficiency is particularly notable in tables with over 500 million rows, where such queries result in the database processing and moving vast amounts of cold metadata that applications do not use, leading to resource over-provisioning and increased latency. By explicitly specifying only the required fields in queries, users can significantly reduce Input/Output operations, preserve buffer pool space, and eliminate the CPU overhead associated with data serialization of unused metadata. The article emphasizes the importance of auditing query performance to identify and refactor high-impact wildcard queries, thereby improving database efficiency and reclaiming optimal performance.

VesselAPI, a maritime data service, transitioned its data platform from MongoDB to TimescaleDB to better handle the unique demands of processing Automatic Identification System (AIS) data, which involves real-time tracking of vessels worldwide. Initially, MongoDB's flexibility was useful for the rapidly changing schema, but it struggled with the time-series and spatial queries that the maritime data required, such as querying vessel positions over time and space. TimescaleDB, a time-series extension of PostgreSQL, provided the necessary features, like automatic partitioning, compression, and spatial querying through PostGIS, all of which aligned more naturally with the data's time-series structure. The company also employed H3, a hexagonal spatial indexing system, to improve query efficiency by pre-filtering location data, thus reducing the load on the spatial index. The migration revealed challenges such as outdated BSON tags in the code, which caused data processing errors, highlighting the importance of thoroughly updating system integrations during such transitions. The move to TimescaleDB allowed VesselAPI to efficiently manage its high volume of AIS data on a single server, streamlining operations that previously required multiple databases and significant application-level workarounds.

Matty Stratton's blog post explores the complexities and hidden costs of using PostgreSQL's Multi-Version Concurrency Control (MVCC) system, particularly for append-only workloads. While MVCC is a remarkable feature that allows PostgreSQL to efficiently handle concurrent read and write operations by maintaining consistent snapshots without locking, it incurs significant overhead. Each database row includes a 23-byte header to manage transaction visibility, leading to increased I/O operations and storage consumption, especially when handling large volumes of immutable data like sensor readings or financial records. The post explains how autovacuum processes, essential for maintaining database health, continue to operate even on append-only tables due to aborted transactions, hint bit setting, and transaction ID freezing. This results in notable write amplification, where considerably more data is written to disk than the actual size of the data inserted. The discussion highlights that while MVCC is integral to PostgreSQL's architecture and cannot be disabled on a per-table basis, alternative storage solutions like TimescaleDB's columnar storage can mitigate these inefficiencies by batching updates and reducing write amplification. This architectural mismatch is significant for users dealing with high-frequency append-only data ingestion, prompting a reevaluation of their database strategy for optimized performance.

Sensor data, often perceived as simple due to its uniform timestamps and readings, actually presents significant challenges when stored with traditional relational database models because these systems are not equipped to handle the continuous, append-only nature of sensor data streams. As the number of devices and the volume of data increase, the limitations of treating sensor data like transactional rows become apparent, leading to issues such as inefficient data ingestion, high storage costs, and problematic query performance. The core insight is that sensor data behaves as a time-series log with decay, meaning its value diminishes over time, necessitating a specialized time-series architecture. This architecture should optimize for sequential data ingestion, time-partitioned storage that aligns with query patterns, and lifecycle tiering to manage data resolution and storage costs as data ages. Transitioning to this model allows developers to focus more on deriving insights from data rather than contending with schema limitations and ensures that systems remain scalable and adaptable as deployments expand.

Sense Manufacturing, co-founded by Farbod Moghaddam, developed an affordable machine monitoring solution called Sense, specifically designed for small and mid-size manufacturers. The team built their technology stack around Tiger Data's time-series database, which was chosen for its ability to handle high-rate data ingestion from thousands of devices, aligning with their need for real-time analytics. The choice of Tiger Cloud allowed them to focus on product development rather than infrastructure management, resulting in a swift transition from development to production in just 60 days. Sense devices provide immediate visibility into machine operations by monitoring power consumption and vibration, enabling shop owners to preemptively address anomalies and reduce downtime. The system, which supports single device start-ups and offers flexible subscription models, has proven to be cost-effective, reducing total costs significantly compared to competitors. By leveraging Tiger Data, Sense aims to expand its capabilities with advanced machine learning techniques to further enhance machine health insights.

A Unified Namespace (UNS) is proposed as a solution for integrating time-series data and equipment context in industrial settings, allowing for streamlined data querying without the need for complex cross-system integration. By using a single PostgreSQL instance with TimescaleDB, both datasets share a query planner, enabling efficient contextualization of industrial data. The UNS schema provides a structured hierarchy for data storage, with specific tables for mapping the ISA-95 hierarchy and storing time-series readings. The database enforces data quality at the schema level, ensuring reliable data analysis and facilitating complex queries that can be used by AI agents. This approach eliminates the need for middleware or application-layer stitching, allowing manufacturers to efficiently query and contextualize data such as equipment usage and performance metrics through simple SQL queries. The architecture supports scalability and operational efficiency, providing a robust foundation for AI-enabled industrial data analysis and decision-making.

Tiger Cloud has introduced significant updates in 2026, focusing on performance, platform reach, developer experience, and infrastructure management. The launch of TimescaleDB 2.25 enhances query speed on compressed data by up to 289x, thanks to a new ColumnarIndexScan execution path and other optimizations, while Postgres 18, now the default for new services, offers improved query performance and security features. The platform extends its reach with features like Azure Marketplace self-serve signup and Tiered Storage on Microsoft Azure, alongside a new AWS region in Zurich. Developer tools have been upgraded with a floating SQL editor, a timeline view for job monitoring, and a CLI/MCP setup guide in the Tiger Console, enhancing workflow efficiency. Additionally, the infrastructure as code capabilities have been expanded with Terraform support for managing S3 source connectors, allowing for comprehensive management of TimescaleDB infrastructure.

Continuous data ingestion presents significant challenges for PostgreSQL, as it disrupts the system's reliance on periodic quiet periods to perform essential maintenance tasks such as autovacuuming, checkpointing, and updating statistics. Unlike batch processing, continuous streams of data lead to constant competition between write operations and maintenance processes, resulting in issues like write latency spikes and database bloat. The core of the problem lies in the Write-Ahead Logging (WAL) mechanism, which imposes a throughput ceiling that is difficult to surpass without substantial hardware upgrades. Common solutions like increasing autovacuum workers or upgrading storage temporarily alleviate symptoms but fail to address the underlying dynamic of unceasing data flow clashing with PostgreSQL’s maintenance requirements. This issue becomes particularly pronounced in environments with continuous data streams, such as IoT systems and financial markets, where data ingestion is relentless and independent of database needs. Recognizing this pattern early can help organizations decide whether to optimize within PostgreSQL's architecture or transition to systems better suited for continuous ingestion workloads.

In the rapidly expanding world of IoT, traditional databases struggle to handle the unique demands of machine-generated data streams, which are characterized by high volume, velocity, and time-based queries. IoT data, produced continuously by devices like industrial sensors and connected vehicles, presents challenges such as evolving device schemas, long data retention with diminishing value, and the need for time-ordered, append-only data handling. Traditional databases often falter as IoT systems move from pilot to production scale, revealing limitations in ingestion capacity, time-range query performance, and storage costs. Scaling these databases through indexing and partitioning may provide temporary relief but fails to address the underlying issues of mismatched design. A time-series-first approach, optimized for IoT workloads, offers a solution with features like time-ordered storage, append-optimized ingestion, and native support for aggregations and lifecycle management. By separating ingestion, storage, and analytics, a modern IoT architecture ensures stable performance and scalability, allowing organizations to efficiently manage and analyze their IoT data.

Doug Pagnutti's exploration into measuring the performance of an Industrial Internet of Things (IIoT) PostgreSQL database reveals complexities in understanding table size, ingest capacity, and query speed. The essay outlines methods for determining table size through row count and disk usage, emphasizing the efficiency of using PostgreSQL's internal metrics for large datasets. It discusses ingest capacity by either analyzing current data streams or stress-testing the database to determine limits, using PostgreSQL's pg_stat_statements to monitor performance. Query speed is addressed by categorizing queries as either wide or deep, suggesting the necessity of regular performance checks to ensure efficiency over time. Pagnutti integrates these measurements to monitor table growth and performance, providing insights into maintaining database reliability as it scales, which is crucial for production systems handling large volumes of IoT data.

In Matty Stratton's article, the challenges of adding indexes in PostgreSQL, particularly in high-frequency data ingestion scenarios, are examined in detail. While indexes can significantly speed up query performance by reducing the need for sequential scans, they also introduce a "write tax" on each row insertion, as every index requires additional write operations. This tax becomes increasingly problematic at high insertion rates, leading to write amplification and increased latency. Timestamp indexes, often used in time-series tables, exacerbate the problem due to their propensity for creating "hot right edges," causing frequent page splits and index bloat. The feedback loop created by adding more indexes to resolve slow queries can, paradoxically, worsen write performance, as it competes with autovacuum processes for I/O resources. Stratton suggests that reconsidering the storage model, such as moving from row-based to columnar storage, can mitigate these issues by reducing write amplification and changing the cost structure of data operations.

In an effort to enhance low-voltage grid capacity and efficiency, Plexigrid, an electrical grid optimization software company, focuses on providing Distribution System Operators (DSOs) with real-time visibility and management tools, leveraging TimescaleDB to optimize grid network utilization. Traditional grid models, which relied on one-way power flows, are being challenged by rising demand and decentralized energy sources like solar and electric vehicles. Plexigrid's solution minimizes the need for costly infrastructure overbuilds by allowing DSOs to operate closer to their existing capacity through real-time data aggregation, enabling dynamic load adjustments and predictive maintenance using digital twins. Transitioning from a four-database system, including InfluxDB, to a unified PostgreSQL/TimescaleDB architecture has markedly improved data ingestion reliability, reduced storage requirements, and enhanced query performance by up to 350 times, facilitating more efficient data management and operational scalability. This strategic shift has provided DSOs with more reliable insights, enhancing grid stability and supporting higher renewable energy integration without extensive infrastructure upgrades.

Deploying Industrial Internet of Things (IIoT) systems typically begins with a pilot project to test the concept, but scaling up often reveals significant challenges, particularly with databases like PostgreSQL. A key issue is the "pilot purgatory" where manufacturers struggle to move beyond initial stages due to the architecture that fails at scale. IIoT data requires continuous, high-volume data ingestion and retention, demanding a robust "performance envelope" to manage storage, ingest rate, and query performance. As data accumulates, databases may face performance degradation, necessitating strategies such as vertical or horizontal scaling, or employing specialized tools like InfluxDB or TimescaleDB to optimize for time-series data. Understanding these performance limits and planning accordingly is crucial for escaping pilot purgatory and ensuring long-term project success.

The blog post by Matty Stratton explores the complexities and trade-offs of using table partitioning in PostgreSQL at scale, emphasizing that while partitioning can significantly enhance performance for time-range queries by reducing the need for extensive data scans, it introduces operational challenges and potential pitfalls. These challenges include the necessity for robust partition automation to prevent data gaps, increased runbook complexity, and the need for extensive monitoring to manage issues such as autovacuum lag and index bloat. The text highlights that partitioning is most beneficial for workloads involving narrow time-range queries and periodic data retention, cautioning that it may only temporarily alleviate problems for high-frequency ingestion workloads with comprehensive query requirements. For long-term scalability, the text suggests considering alternative solutions like TimescaleDB, which can offer improved performance and reduced storage needs without overhauling existing SQL and tooling.

The article provides a comprehensive guide on efficiently building an MQTT-to-TimescaleDB pipeline for IoT sensor data ingestion, emphasizing that the typical per-message insert approach fails under high load due to the structural mismatch between continuous MQTT streams and SQL's preference for batch writes. It suggests using a batching strategy that involves buffering incoming messages and periodically flushing them to the database as a solution, thus reducing transaction overhead and improving throughput. The article details the necessary schema design for effective batching, introduces techniques for handling edge cases like duplicates and malformed messages, and explores methods to manage back-pressure and horizontal scaling. It highlights the importance of a well-designed batching system as a foundation for scaling IoT data pipelines, and recommends monitoring, compression, and query optimization for production deployments, while also providing insights into handling various failure modes and tuning the pipeline for optimal performance.

MarketReader, a fintech startup founded in 2021, is dedicated to providing US market trading insights by processing approximately 3 million trades per minute using TimescaleDB and Tiger Data's time-series capabilities. This setup allows them to deliver real-time analytics and reduce market noise for clients like retail brokerages and institutional investors. MarketReader's architecture leverages AWS for elastic compute and orchestration, Redis for messaging, and Supabase for caching, with Tiger Cloud serving as the primary data storage and analytics platform. The system is designed to detect unusual stock behavior and provide contextually rich insights by integrating vector search and large language models, which attach semantically related news and social signals to market movements. This modular and scalable approach not only enhances MarketReader's ability to offer timely and accurate insights but also minimizes operational overhead, allowing the company to focus on expanding its product capabilities and collaborating with investment firms to improve chatbot intelligence and market context for large language models.