Pinot can filter and aggregate petabyte data sets with P90 latencies in the tens of milliseconds—fast enough to return live results interactively in the UI.
With user-facing applications querying Pinot directly, it can serve hundreds of thousands of concurrent queries per second.
Ingest the same record many times, but see only the latest value at query time. Upserts are built-in and production-tested since version 0.6.
Perform arbitrary fact/dimension and fact/fact joins on petabyte data sets.
Built for scale
Pinot is horizontally scalable and fault-tolerant, adaptable to workloads across the storage and throughput spectrum.
SQL query interface
The highly standard SQL query interface is accessible through a built-in query editor and a REST API.
Manage and secure data in isolated logical namespaces for cloud-friendly resource management.
Building Latency Sensitive User Facing Analytics via Apache Pinot
Using Apache Kafka and Apache Pinot for User-Facing Analytics
- Using Helm
- Using Binary
- Build From Source
helm repo add pinot https://raw.githubusercontent.com/apache/pinot/master/helmkubectl create ns pinothelm install pinot pinot/pinot -n pinot --set cluster.name=pinot
VERSION=1.0.0wget https://downloads.apache.org/pinot/apache-pinot-$VERSION/apache-pinot-$VERSION-bin.tar.gztar vxf apache-pinot-*-bin.tar.gzcd apache-pinot-*-binbin/quick-start-batch.sh
# Clone a repogit clone https://github.com/apache/pinot.gitcd pinot # Build Pinotmvn clean install -DskipTests -Pbin-dist # Run the Quick Democd pinot-distribution/target/apache-pinot-*-SNAPSHOT-bin/apache-pinot-*-SNAPSHOT-binbin/quick-start-batch.sh