Arpit's Newsletter read by 90000+ engineers
Weekly essays on real-world system design, distributed systems, or a deep dive into some super-clever algorithm.
Managing massive, talking hundreds of terabytes here, Search clusters is no joke, especially at @Twitter’s scale.
To manage them efficiently, Twitter built a bunch of toolings, here’s a quick gist about it 🧵👇
Twitter uses ES to power the search of tweets, users, and DMs. ES gives them the necessary speed, performance, and horizontal scalability.
Given massive adoption, they needed to ensure the efficiency, and stability of these clusters and provide some standardized way of access.
The Twitter team built a simple proxy for Elasticsearch that transparently sits in front of the Elasticsearch cluster.
The proxy is an extremely simple and lightweight TCP and HTTP-based relay that…
in a standard way, captures all critical metrics like - cluster health, latency, success, and failure rates here; along with this we can also
ES performance degrades when there is a massive surge in traffic. We typically see an
But it is a common usecase for Twitter to ingest massive data (tweets) every now and then, hence they tweaked the ingestion…
The write requests that come to the ES proxy are sent to Kafka. Consumers read from Kafka and relay them to the ES cluster.
Doing it asynchronously allows us to
Twitter has a constant need of ingesting 100s of TBs of data in the Elasticsearch clusters.
Doing massive ingestion through Map Reduce jobs directly on ES will take down the entire cluster and doing it through Kafka makes it unnecessarily granular;
hence a backfill service …
The backfill indexing requests are dumped on an HDFS.
The requests are partitioned and read using distributed jobs and indexed in Elasticsearch.
A separate orchestrator computes the number of workers required to consume the indexing requests.
Here's the video ⤵
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Arpit's Newsletter read by 90000+ engineers
Weekly essays on real-world system design, distributed systems, or a deep dive into some super-clever algorithm.