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Tag: Benchmarks

How We Learned to Stop Guessing and Love Low P-Values

How We Learned to Stop Guessing and Love Low P-Values

“I don’t think it’s quite fair to condemn the whole program because of a single slip up, sir.” Famous last words from Kubrick’s 1964 classic Dr. Strangelove, as a forlorn general realizes his negligence is about to lead to nuclear apocalypse, may feel relatable to any engineer who has immediately regretted pushing the big red “release” button only to later find themselves putting out fires late into the night. Sometimes the answer is simply “write more tests!” or “write better tests!”,

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The Effect of Isolation Levels on Distributed SQL Performance Benchmarking

The Effect of Isolation Levels on Distributed SQL Performance Benchmarking

This post addresses a concern raised about a benchmarking result we recently published comparing the performance of YugabyteDB, Amazon Aurora and CockroachDB. It was pointed out that we unfairly used the default isolation level for each database rather than use serializable isolation level in all databases (even though serializable level was not required for these workloads). In addition, we are also happy to share additional results with the workloads run at YugabyteDB’s serializable isolation level.

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YugaByte Database Engineering Update – Nov 27, 2018

YugaByte Database Engineering Update – Nov 27, 2018

Lots has happened since our last engineering update about 3 months ago. Below are some of the highlights.

PostgreSQL API Updates & PostgresConf Silicon Valley Wrap-Up

We have made a lot of progress on YSQL, the PostgreSQL compatible distributed SQL API for YugabyteDB! You can also read about YSQL architecture which covers how distributed SQL is implemented in YugabyteDB.

We were at the first ever PostgresConf Silicon Valley in October 2018.

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Benchmarking an 18 Terabyte YugabyteDB Cluster with High Density Data Nodes

Benchmarking an 18 Terabyte YugabyteDB Cluster with High Density Data Nodes

For ever-growing data workloads such as time series metrics and IoT sensor events, running a highly dense database cluster where each node stores terabytes of data makes perfect sense from a cost efficiency standpoint. If we are spinning up new data nodes only to get more storage-per-node, then there is a significant wastage of expensive compute resources. However, running multi-terabyte data nodes with Apache Cassandra as well as other Cassandra-compatible databases (such as DataStax Enterprise) is not an option.

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