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Transaction Data with TimescaleDB

Implementing TimescaleDB for Enhanced Data Management at Tickr

At Tickr, we are committed to optimizing the process of ingesting, analyzing, and enhancing our customers’ data with AI-driven insights. Time series data forms a critical component of business analytics, and in our pursuit of efficiency, we chose to evaluate TimescaleDB. Our objective was to leverage its reputed benefits in query performance and disk space utilization, which are crucial for our large-scale forecasting services and client deliverables that demand agility and precision.

Elevating Data Handling Capabilities

Our goal was to harness a robust solution that could manage extensive time series data with ease. We required a system that could seamlessly integrate with our existing infrastructure and elevate our capability to process large volumes of transaction data efficiently.

Transitioning to TimescaleDB

We selected TimescaleDB for its promising blend of reliability and innovation, as it is an extension of PostgreSQL, a trusted and widely-used database system. This allowed us to adopt new technology while remaining grounded in a familiar environment. Our Kubernetes cluster, hosted on EKS, provided the foundation for this transition, allowing us to deploy TimescaleDB with minimal friction.

Data and Disk Management Issues

The migration process was not without its hurdles. The initial attempt to create a hypertable for our roughly 150GB of transaction data, consisting of nearly 700 million rows, led to a significant error due to disk space constraints. A closer examination revealed issues related to the Write-Ahead Logging (WAL) system and data directory sizes that exceeded expectations.

Resolving Issues and Reaping Benefits

We tackled these challenges by monitoring and adjusting the WAL, identifying inactive replication slots, and managing database copies more effectively. These measures not only resolved our immediate issues but also provided us with a deeper understanding of PostgreSQL’s behavior in high-demand scenarios.

After resolving the initial setbacks, the implementation of TimescaleDB’s hypertables and compression techniques resulted in a 70% reduction in disk space usage. Queries that previously took around two hours were now executed in approximately 10 seconds—a monumental increase in efficiency.

Why TimescaleDB’s Advantages Matter

The dramatic improvement in query performance from hours to seconds is more than a technical victory; it represents a transformative change in how we can serve our clients. Swift data retrieval allows us to provide timely insights, enhance our forecasting accuracy, and respond to client needs with unprecedented speed. The disk space savings translate to cost efficiency, enabling us to scale our services without proportionally increasing our infrastructure costs.

These improvements directly contribute to our business goals by enabling us to handle larger datasets, deliver faster results, and operate more cost-effectively. In turn, this empowers our clients with the agile, data-driven decision-making they require to stay competitive.

Invitation for Dialogue

We believe that sharing knowledge fosters innovation and growth. If you’re interested in discussing our experience with TimescaleDB or have questions about its implementation and benefits, we welcome you to reach out. Our journey may provide valuable insights for those considering similar technological advancements or facing comparable data management challenges.

We invite you to join us in a conversation that could illuminate your path to optimized data solutions and business success.

Publish Date
October 11th, 2023
Time series data forms a critical component of business analytics, and in our pursuit of efficiency, we chose to evaluate TimescaleDB.
Ken Koski
Ken Koski

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