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Published on Sep 17, 2019
Elasticsearch is a distributed search and analytics engine based on Apache Lucene. Initially released in 2010, it has quickly become the most popular enterprise search engine, and is commonly used for log analytics, full-text search, operational and security intelligence, business analytics, and metrics use cases. Built as a distributed system since its inception, Elasticsearch effortlessly scales to clusters of hundreds of servers. As part of a larger resiliency effort, Elasticsearch’s core cluster coordination and data replication algorithms have undergone major overhauls in recent years. The use of formal methods, with in particular TLA + , PlusCal and the TLC model checker, has extensively contributed to the goal of creating a much safer and resilient system. This presentation will provide an overview on the various uses of TLA + during the development of Elasticsearch:
We refined and validated an informal specification of the data replication algorithm that has been powering Elasticsearch since version 6, released in November 2017, by creating a TLA + specification during the implementation effort. This algorithm, based on the primary-backup approach, enables high-throughput sharded data ingestion pipelines and has served as fundational work to build newer features such as cross- datacenter replication.
We studied two bugs in a highly concurrent component that handles deletion tomb- stones and out-of-order writes under various optimizations. The implementation was mapped onto a PlusCal spec, which, with the use of the TLC model checker, led us to discover an additional unknown bug in the implementation, which we only later observed in the wild. The bug fixes were first prototyped using the PlusCal specification.
We designed a new cluster coordination subsystem and validated it with TLA + before starting off our implementation efforts. This new cluster coordination subsystem is powering all Elasticsearch clusters since version 7, released in April 2019. The TLA + specification is modeling the core safety bits of the consensus algorithm with dynamic reconfiguration. The implementation of this core safety module has a direct one-to-one mapping with the TLA + specification.
Our chaos-monkey style test infrastructure discovered a bug in the cluster state storage layer that was related to the atomic persistence of state. This only surfaced through randomized testing after running for months on our CI infrastructure. The implementation was mapped onto a TLA + model to rule out other such bugs. The TLC model checker was able to find this bug within seconds.
All Elasticsearch TLA + specifications are open-source and publicly available on our Github repository, just as the actual system that is modeled.