Thoughts on open source, cloud native and distributed databases
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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Read MoreACID 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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Read MoreProf. 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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Read MoreAt 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:
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Read MoreAs 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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Read MoreWhether 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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Read MoreOur 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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Read MoreAs 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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Read MoreACID 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 Amazon DynamoDB and Apache Cassandra initially focused on “big data” use cases that did not require such guarantees and hence avoided implementing them altogether. However, ACID transactions are making a strong comeback in the last 2 years with the launch of next-generation distributed databases that have built-in support for them.
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Read MoreModern app architectures rely on data with different models and access patterns. Polyglot persistence, first introduced in 2011, states that each such data model should be powered by an independent database that is purpose-built for that model. The original intent was to look beyond relational/SQL databases to the emerging world of NoSQL.
Polyglot Persistence in Action at an E-Commerce App (Source: Martin Fowler)
Polyglot persistence is not free of costs — it leads to increased complexity across the board.
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