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Pinterest Switches from OpenTSDB to Their Own Time Series Database

by Hrishikesh Barua Follow 15 Followers on  Sep 16, 2018

The Pinterest engineering team has used OpenTSDB for storing and querying metrics since 2014. Recently, they developed and switched to their own time series database called Goku to mitigate various performance issues in OpenTSDB caused by a growth in the amount of metrics data.

DevOps Follow 972 Followers

Uber Open Sources Its Large Scale Metrics Platform M3

by Hrishikesh Barua Follow 15 Followers on  Aug 18, 2018 1

Uber’s engineering team released its metrics platform M3 as open source which it has been using internally for some years. The platform was built to replace its Graphite based system, and provides cluster management, aggregation, collection, storage management, a distributed time series database (TSDB) and a query engine with its own query language M3QL.

DevOps Follow 972 Followers

How SendGrid Scales Its Email Delivery Systems

by Hrishikesh Barua Follow 15 Followers on  Jul 28, 2018 2

SendGrid, a cloud based email service, has seen its backend architecture evolve from a small Postfix installation to a system hosted on their own data-centers as well as on the public cloud. Rewriting of services in Go, a gradual move to AWS, and a distributed Ceph-based queue allows the team to hand over 40 billion emails per month.

DevOps Follow 972 Followers

Bloomberg’s Standardization and Scaling of Its Monitoring Systems

by Hrishikesh Barua Follow 15 Followers on  Jul 21, 2018

One of the outcomes of Bloomberg’s adoption of SRE practices across its development teams is the monitoring system, backed by the Cassandra-based Metrictank time-series database, that they put in place.

DevOps Follow 972 Followers

Monitoring Distributed Task Queues at MeilleursAgents

by Hrishikesh Barua Follow 15 Followers on  Feb 18, 2018

MeilleursAgents, a website that lets property sellers list and get an estimated price of their property, shared details of how their Celery-based distributed task queue is monitored. A combination of Python, StatsD, Bucky, Graphite and Grafana form the pipeline to monitor task lifecycle and execution rates.

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