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I love postgres for most things but these days (Especially while my product is in development, embedded, or just not internet-facing) sqlite is amazingly workable.
Killer postgres features however: Row-level security (Amazing when you're using something like postgrest for rapid application development [1]), and its built in fulltext search engine is 'good enough' for use cases like when you have an enormous users table and need to index something simple enough, like email addresses for quick login.
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InfluxDB
Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.
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Messaging is the first use-case given by the Kafka docs [1] and is the first core capability they mention on the Kafka landing page [2].
1. https://kafka.apache.org/documentation/#uses_messaging
I think use (and think of) Kafka as a message bus, with distributed commit logging being the mechanism for how it accomplishes that task.
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postgres-operator
Postgres operator creates and manages PostgreSQL clusters running in Kubernetes (by zalando)
My favorite route right now is running a postgres operator on Kubernetes & letting it do all the work for me.
Zalando's operator use Patroni under the hood, to create a cluster over streaming replication. It also has Spilo, which orchestrates pg_basebackup or WAL-E for point-in-time backup. https://github.com/zalando/postgres-operator#postgresql-feat...
CrunchyData operator seems to have built their own streaming replication system coordinated by Raft. https://access.crunchydata.com/documentation/postgres-operat...
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As always: it depends. For some workloads something like Citus [1] might allow you stay within the PostgreSQL ecosystem even when you are trying to do OLAP.
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postgresql handles queues well. the trick is skip lock.
https://github.com/jmscott/talk/blob/master/ams-pgopen-20150...
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SonarLint
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