Performance Benchmarks

GO-JEK’s Performance Benchmarking of CockroachDB, TiDB & YugabyteDB on Kubernetes

GO-JEK’s Performance Benchmarking of CockroachDB, TiDB & YugabyteDB on Kubernetes

Iqbal Farabi and Tara Baskara, Systems Engineers from GO-JEK Indonesia, recently presented the results of their benchmarking of cloud native databases on Kubernetes at KubeCon Europe in Barcelona. The three databases they benchmarked were CockroachDB, TiDB and YugabyteDB. This post brings their presentation (video recording) and slides (PDF) to the attention of our readers. It also highlights a few areas of collaboration between the GO-JEK team and YugabyteDB Engineering.

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Announcing YugabyteDB 1.2 and Company Update

Announcing YugabyteDB 1.2 and Company Update

The team at YugaByte is excited to announce that YugabyteDB 1.2 is officially GA! You can download the latest version from our Quick Start page.

New in 1.2: YugaByte SQL Beta 3

YugaByte SQL (YSQL) is our PostgreSQL v11 compatible, distributed SQL API. It is ideal for powering microservices that require low latency, internet scale, geographic data distribution and extreme resilience to failures but want the data modeling flexibility of SQL (joins,

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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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Distributed Backups in Multi-Region YugabyteDB Clusters

Distributed Backups in Multi-Region YugabyteDB Clusters

Our post Getting Started with Distributed Backups in YugabyteDB details the core architecture powering distributed backups in YugabyteDB. It also highlights a few backup/restore operations in a single region, multi-AZ cluster. In this post, we perform distributed backups in a multi-region YugabyteDB cluster and verify that we achieve performance characteristics similar to those observed in a single region cluster.

We configured a 9 node cluster with 3 availability zones across 2 regions and repeated the benchmark introduced in the post.

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Getting Started with Distributed Backups in YugabyteDB

Getting Started with Distributed Backups in YugabyteDB

YugabyteDB is a distributed database with a Google Spanner-inspired strongly consistent replication architecture that is purpose-built for high availability and high performance. This architecture allows administrators to place replicas in independent fault domains, which can be either availability zones or racks in a single region or different regions altogether. These types of multi-AZ or multi-region deployments have the immediate advantage of guaranteeing organizations a higher order of resilience in the event of a zone or region failure.

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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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Distributed ACID Transactions with High Performance

Distributed ACID Transactions with High Performance

ACID transactions are a fundamental building block when developing business-critical, user-facing applications. They simplify the complex task of ensuring data integrity while supporting highly concurrent operations. While they are taken for granted in monolithic SQL/relational databases, distributed NoSQL/non-relational databases either forsake them completely or support only a highly restrictive single-row flavor (see sections below). This loss of ACID properties is usually justified with a gain in performance (measured in terms of low latency and/or high throughput).

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Achieving Sub-ms Latencies on Large Datasets in Public Clouds

Achieving Sub-ms Latencies on Large Datasets in Public Clouds

One of our users was interested to learn more about YugabyteDB’s behavior for a random read workload where the data set does not fit in RAM and queries need to read data from disk (i.e. an uncached random read workload).

The intent was to verify if YugabyteDB was designed well to handle this case with the optimal number of IOs to the disk subsystem.

This post is a sneak peak into just one of the aspects of YugabyteDB’s innovative storage engine,

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