The YugaByte Database Blog

Thoughts on open source, cloud native and distributed databases

Rise of Globally Distributed SQL Databases – Redefining Transactional Stores for Cloud Native Era

At last month’s KubeCon + CloudNativeCon in Seattle, the single biggest change from previous container-related conferences was the excitement among the end user companies around their adoption of Kubernetes and the associated cloud native infrastructure ecosystem. The CNCF End User Community page today lists 50+ enterprises and 21+ case studies including those from industry bellwethers such as Capital One,

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YugaByte Announces Kubernetes StatefulSets Support to Enable Scale-Out PostgreSQL Deployments

YugaByte is excited to be at KubeCon today to announce Kubernetes StatefulSets support for our SQL API which complements the NoSQL APIs already generally available. YSQL is YugaByte DB’s PostgreSQL-compatible Distributed SQL API (currently in Beta). This new feature, available in YugaByte DB 1.1.7, cloud-native applications and microservices can rely on SQL and NoSQL to take full advantage of Kubernetes StatefulSets to power horizontally scalable,

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Are MongoDB’s ACID Transactions Ready for High Performance Applications?

Web app developers initially adopted MongoDB for its ability to model data as “schemaless” JSON documents. This was a welcome relief to many who were previously bitten by the rigid structure and schema constraints of relational databases. However, two critical concerns that have been a thorn on MongoDB’s side over the years are that of data durability and ACID transactions.

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Apache Cassandra: The Truth Behind Tunable Consistency, Lightweight Transactions & Secondary Indexes

ACID transactions were a big deal when first introduced formally in the 1980s in monolithic SQL databases such as Oracle and IBM DB2. Popular distributed NoSQL databases of the past decade including Apache Cassandra initially focused on “big data” use cases that did not require such guarantees and hence avoided implementing them altogether. Our post, “A Primer on ACID Transactions: The Basics Every Cloud App Developer Must Know”

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Google Spanner vs. Calvin: Is There a Clear Winner in the Battle for Global Consistency at Scale?

Prof. Daniel Abadi, lead inventor of the Calvin transaction management protocol and the PACELC theorem, wrote a thought-provoking post last month titled “NewSQL database systems are failing to guarantee consistency, and I blame Spanner”. The post takes a negative view of software-only Google Spanner derivative databases such as YugaByte DB and CockroachDB that use Spanner-like partitioned consensus for single shard transactions and a two phase commit (2PC) protocol for multi-shard (aka distributed) transactions.

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Jepsen Testing on YugaByte DB

At YugaByte, our mission is to build a robust, reliable, distributed OLTP database. Needless to say, we take correctness and technical accuracy of our claims very seriously. Therefore, we absolutely love a testing framework like Jepsen which helps verify correctness and are fans of Kyle Kingsbury’s work!

Here is a summary of what we have done so far in regards to Jepsen:

  • We have performed our own DIY style Jepsen testing
  • The YugaByte DB Jepsen testing repository is open source
  • For the current suite of Jepsen tests for YugaByte DB that we have tested in a loop,

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How Does the Raft Consensus-Based Replication Protocol Work in YugaByte DB?

As we saw in ”How Does Consensus-Based Replication Work in Distributed Databases?”, Raft has become the consensus replication algorithm of choice when it comes to building resilient, strongly consistent systems. The YugaByte DB database uses Raft for both leader election and data replication. Instead of having a single Raft group for the entire dataset in the cluster,

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How Does Consensus-Based Replication Work in Distributed Databases?

Whether it be a WordPress website’s MySQL backend or Dropbox’s multi-exabyte storage system, data replication is at the heart of making data durable and available in the presence of hardware failures such as machine crashes, disk failures, network partitions and clock skews. The basic idea behind replication is very simple: keep multiple copies of data on physically isolated hardware so that the failure in one does not impact the others and as a result,

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Implementing Distributed Transactions the Google Way: Percolator vs. Spanner

Our post 6 Signs You Might be Misunderstanding ACID Transactions in Distributed Databases describes the key challenges involved in building high performance distributed transactions. Multiple open source ACID-compliant distributed databases have started building such transactions by taking inspiration from research papers published by Google. In this post, we dive deeper into Percolator and Spanner, the two Google systems behind those papers,

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6 Signs You Might be Misunderstanding ACID Transactions in Distributed Databases

As described in A Primer on ACID Transactions, first generation NoSQL databases dropped ACID guarantees with the rationale that such guarantees are needed only by old school enterprises running monolithic, relational applications in a single private datacenter. And the premise was that modern distributed apps should instead focus on linear database scalability along with low latency, mostly-accurate,

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