Thoughts on distributed databases, open source, and cloud native
This is the first in a two part blog series which highlights factual errors in the Cockroach Labs analysis of YugabyteDB. In the second post in this series, we provide the next layer of detail behind YugabyteDB’s architecture, with an emphasis on comparing it to that of CockroachDB’s.
At Yugabyte,
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Read MoreA pushdown is an optimization to improve the performance of a SQL query by moving its processing as close to the data as possible. Pushdowns can drastically reduce SQL statement processing time by filtering data before transferring it over the network, filtering data before loading it into memory, or pruning out entire files or blocks that do not need to be read.
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When starting the YugabyteDB project, our founding thesis was to build a high-performance distributed SQL database for the cloud native era. Achieving high performance will always remain an ongoing initiative, especially when additional optimizations are required to support new features and new use cases. We are excited that the current YugabyteDB 2.1 release has a number of improvements that make Yugabyte SQL’s performance 10x better on average than the previous 2.0 release (from September 2019).
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Read MoreA distributed SQL database needs to automatically partition the data in a table and distribute it across nodes. This is known as data sharding and it can be achieved through different strategies, each with its own tradeoffs. In this post, we will examine various data sharding strategies for a distributed SQL database, analyze the tradeoffs, explain the rationale for which of these strategies YugabyteDB supports and what we picked as the default sharding strategy.
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Read MoreEnterprises deploy YugabyteDB clusters across multiple availability zones (AZs) in order to ensure continuous availability of their business-critical services even when faced with cloud infrastructure failures like zone outages. On November 12, 2019, there was one such outage of an entire availability zone in the eu-central-1 region of AWS. This was reported on the AWS status page on that day,
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Read MoreThere are a number of well-known experiments where eventually-consistent NoSQL databases were scaled out to perform millions of inserts and queries. Here, we do the same using YSQL, YugabyteDB’s PostgreSQL-compatible, strongly-consistent, distributed SQL API. We created a 100-node YugabyteDB cluster, ran single-row INSERT and SELECT workloads with high concurrency – each for an hour and measured the sustained performance (throughput and latency).
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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,
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Update: A new post “The Effect of Isolation Levels on Distributed SQL Performance Benchmarking” includes performance results from running these workloads at serializable isolation level in YugabyteDB.
We are excited to announce the general availability of YugabyteDB 2.0 this week! One of the flagship features of the release was the production readiness of the PostgreSQL-compatible YugabyteDB SQL (YSQL) API.
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Read MoreNote: You can join the discussion on Hacker News here.
We are very excited to announce that the SQL API of YugabyteDB v1.3.1.2 passed Jepsen testing performed by Kyle Kingsbury [Edit] (with the exception of transactional DDL support, which almost no other distributed SQL database vendor supports,
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Read MoreNote: This post contains interactive animations that explain how some of these complex algorithms work. Please view this post in a suitable media (at least 1000px by 600px screen resolution) for best results.
In this blog post, we are going to dive deep into the read performance of Raft – why read performance can take a hit and how it can be improved using leader leases.
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