The stark difference in performance translates into a large cost differential as well. However, as you’ll see, if you have a more traditional client-server data-collection architecture, or one using a more common streaming pipeline with database consumers, like Apache Kafka, TimescaleDB can import more than three million metrics per second from one client-and doesn’t need 33,000 clients.īecause performance benchmarking is complex, we share the details of our setup, configurations, and workload patterns later in this post, as well as instructions on how to reproduce them. If you have an edge-based IoT system that pre-computes metrics on thousands of edge nodes before sending them, Amazon Timestream could simplify your data collection architecture. It’s certainly impressive to support 33,000 connections per second without issue, and this demonstrates one of the key advantages that Amazon presents with a serverless architecture like Timestream. To achieve three billion metrics/hour in their test, four million hosts sent 26 metrics every two minutes, an average of 33,000 hosts reporting 866,667 metrics every second. Just look at all of the databases we created through the process!Īfter all of our attempts to achieve better Amazon Timestream performance, we were even more confused when we read a recent post on the AWS Database Blog that discusses achieving ingest speeds of three billion metrics/hour.Īlthough the details of how they ingested this scale of data aren’t completely clear, it appears that each “monitored host” sent individual metrics at various intervals directly to Amazon Timestream. We REALLY tried to get Amazon Timestream to perform better. Although feedback was hard to find, we weren’t the only ones seeing these performance results, as evidenced by a similar benchmark by Crate.io. We even posted on Reddit to see if others had been able to get better performance with Amazon Timestream. These results were so dramatic that we did not believe them at first, and we tried a variety of workloads and settings to ensure we weren’t missing anything. Note: Several queries’ ratios (high-cpu-all, lastpoint, groupby-orderby-limit) are “undefined” because Amazon Timestream did not finish executing them within the default 60-second timeout period that Timestream imposes, while TimescaleDB completed them in less than a single second Results of benchmarking query performance between TimescaleDB and Amazon Timestream In particular, there were workloads and query types easily supported by TimescaleDB that Amazon Timestream was unable to handle. TimescaleDB outperformed Amazon Timestream 6,000x on inserts and 5-175x on queries, depending on the query type. For those interested, we go into much more detail later in this post. We compare TimescaleDB and Amazon Timestream across several dimensions:īelow is a summary of our results. Amazon Timestream customers include Autodesk, PubNub, and Trimble. Amazon Timestream not only shares a similar name to TimescaleDB, but also embraces SQL as its query language. This is Amazon’s time-series database-as-a-service. The TimescaleDB community has become the largest developer community for time-series data: tens of millions of downloads over 500,000 active databases organizations like AppDynamics, Bosch, Cisco, Comcast, Credit Suisse, DigitalOcean, Dow Chemical, Electronic Arts, Fujitsu, IBM, Microsoft, Rackspace, Schneider Electric, Samsung, Siemens, Uber, Walmart, Warner Music, WebEx, and thousands of others (all in addition to the PostgreSQL community and ecosystem).Īmazon Timestream was first announced at AWS re:Invent November 2018, but its launch was delayed until September 2020. TimescaleDB, first launched in April 2017, is today the industry-leading relational database for time-series, open-source, engineered on top of PostgreSQL, and offered via download or as a fully managed service on AWS. But if you let the analysis speak for itself, you’ll find that we stay as objective as possible and aim to be fair to Amazon Timestream in our testing and results reporting.Īlso, if you want to check our work or run your own analysis, we provide all our testing via the Time-Series Benchmark Suite, an open-source project that anyone can use and contribute to. Yes, we are the developers of TimescaleDB, so you might quickly disregard our comparison as biased. This post compares TimescaleDB and Amazon Timestream across quantitative and qualitative dimensions.
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