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Pinot is a realtime distributed OLAP datastore, which is used at LinkedIn to deliver scalable real time analytics with low latency. It can ingest data from offline data sources (such as Hadoop and flat files) as well as online sources (such as Kafka). At LinkedIn, it powers dozens of both internal and customer-facing analytical applications, such as profile and page views, with interactive-level response times.
In many analytical applications, having a low latency between events occurring and them being queryable opens new possibilities for data analysis. Pinot allows near real time ingestion of events through Kafka as well as batch processing through Hadoop. Because data sizes and query rates can vary a lot between applications, Pinot is designed to scale horizontally and query data sets with billions of rows with sub second latency.
Pinot is queried through the Pinot Query Language (PQL), which is a query language similar to SQL. For example, the following query behaves as one would normally expect:
SELECT SUM(saleValue) FROM sales
WHERE year BETWEEN 2012 AND 2015
AND quarter = 'Q4'
GROUP BY region, departmentPinot is well suited for analytical use cases on immutable append-only data that require low latency between an event being ingested and it being available to be queried. Because of the design choices to achieve these goals, there are certain limitations present in Pinot:
- Fact tables
- Selection and aggregation on fact tables
- Near real time fact ingestion and querying
- Multivalue fields
- Joins with other tables (joins with small dimension tables are part of the future roadmap)
- User defined functions
- Nested data types
- Being a system of reference (Pinot can contain important data, but it should not be relied upon as a source of truth; for example, it does not have any kind of backup mechanism and it is assumed that data can simply be regenerated from another system, such as Hadoop, and uploaded again in Pinot)
- Full text search
- Transactions
- Individual row update and deletion
Some of the use cases where Pinot worked well for us is makings events queryable (profile views, ad campaign performance, etc.) in an analytical fashion (who viewed this profile in the last weeks, how many ads were clicked per campaign). It would not be appropriate for storing data that is expected to be updated (user profiles, messages) or can be stored more efficiently in other systems (such as key-value stores like Voldemort or Redis).
- Java 7 or later
- Zookeeper
Steps to launch the Pinot cluster, upload and query data are outlined below. For detailed description refer to documentation:
- Start the cluster: This includes starting Zookeeper, Pinot-Controller, Pinot-Broker, PinotServer:
pinot-admin.sh StartZookeeper &
pinot-admin.sh StartController -controllerPort 9000 \
-zkAddress "localhost:2181" -dataDir "/tmp/PinotController &
pinot-admin.sh StartBroker -brokerPort 8099 \
-zkAddress "localhost:2181" &
pinot-admin.sh StartServer -serverPort 8098 \
-zkAddress "localhost:2181" -dataDir /tmp/data \
-segmentDir /tmp/segment &- Create Pinot segments from JSON/AVRO/CSV
pinot-admin.sh CreateSegment -dataDir ./data -format CSV \
-tableName myTable -segmentName mySegment \
-schemaFile ./data/schema.json \
-outDir ./data/pinotSegments -overwrite- Add table, upload and query data
pinot-admin.sh AddTable -filePath ./data/table.json \
-controllerPort 9000
pinot-admin.sh UploadSegment -controllerPort 9000 \
-segmentDir ./data/pinotSegments
pinot-admin.sh PostQuery -brokerUrl http://localhost:8099 \
-query "select count(*) from 'myTable'"- Stop all processes:
pinot-admin.sh StopProcess -server -broker -controller -zooKeeper