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Scaling Graphite for Metrics Collection on AWS at Teads Engineering
Teads Engineering shared their story of scaling Graphite deployment from a single server, trying out various approaches like BigGraphite, and finally settling on the go-graphite stack and a custom HA architecture on AWS.
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Scaling Graphite at Booking.com
Booking.com's engineering team scaled their Graphite deployment from a small cluster to one that handles millions of metrics per second. Along the way, they modified and optimized Graphite's core components - the carbon-relay and carbon-cache, and the rendering API.
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Pinterest Switches from OpenTSDB to Their Own Time Series Database
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.
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Uber Open Sources Its Large Scale Metrics Platform M3
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.
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How SendGrid Scales Its Email Delivery Systems
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.
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Bloomberg’s Standardization and Scaling of Its Monitoring Systems
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.
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Monitoring Distributed Task Queues at MeilleursAgents
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.