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Configuration Reference
Running Pinot in Production
Requirements
You will need the following in order to run pinot in production:
- Hardware for controller/broker/servers as per your load
- Working installation of Zookeeper that Pinot can use. We recommend setting aside a path within zookpeer and including that path in pinot.controller.zkStr. Pinot will create its own cluster under this path (cluster name decided by pinot.controller.helixClusterName)
- Shared storage mounted on controllers (if you plan to have multiple controllers for the same cluster). Alternatively, an implementation of PinotFS that the Pinot hosts have access to.
- HTTP load balancers for spraying queries across brokers (or other mechanism to balance queries)
- HTTP load balancers for spraying controller requests (e.g. segment push, or other controller APIs) or other mechanisms for distribution of these requests.
Deploying Pinot
In general, when deploying Pinot services, it is best to adhere to a specific ordering in which the various components should be deployed. This deployment order is recommended in case of the scenario that there might be protocol or other significant differences, the deployments go out in a predictable order in which failure due to these changes can be avoided.
The ordering is as follows:
- 1.pinot-controller
- 2.pinot-broker
- 3.pinot-server
- 4.pinot-minion
Managing Pinot
Pinot provides a web-based management console and a command-line utility (
pinot-admin.sh) in order to help provision and manage pinot clusters.Pinot Management Console
The web based management console allows operations on tables, tenants, segments and schemas. You can access the console via
http://controller-host:port/help. The console also allows you to enter queries for interactive debugging. Here are some screen-shots from the console.
Listing all the schemas in the Pinot cluster:
Rebalancing segments of a table:
Command line utility (pinot-admin.sh)
The command line utility (
pinot-admin.sh) can be generated by running mvn install package -DskipTests -Pbin-dist in the directory in which you checked out Pinot.Here is an example of invoking the command to create a pinot segment:
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$ ./pinot-distribution/target/apache-pinot-0.8.0-SNAPSHOT-bin/apache-pinot-0.8.0-SNAPSHOT-bin/bin/pinot-admin.sh CreateSegment -dataDir /Users/host1/Desktop/test/ -format CSV -outDir /Users/host1/Desktop/test2/ -tableName baseballStats -segmentName baseballStats_data -overwrite -schemaFile ./pinot-distribution/target/apache-pinot-0.8.0-SNAPSHOT-bin/apache-pinot-0.8.0-SNAPSHOT-bin/sample_data/baseballStats_schema.json
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Executing command: CreateSegment -generatorConfigFile null -dataDir /Users/host1/Desktop/test/ -format CSV -outDir /Users/host1/Desktop/test2/ -overwrite true -tableName baseballStats -segmentName baseballStats_data -timeColumnName null -schemaFile ./pinot-distribution/target/apache-pinot-0.8.0-SNAPSHOT-bin/apache-pinot-0.8.0-SNAPSHOT-bin/sample_data/baseballStats_schema.json -readerConfigFile null -enableStarTreeIndex false -starTreeIndexSpecFile null -hllSize 9 -hllColumns null -hllSuffix _hll -numThreads 1
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Accepted files: [/Users/host1/Desktop/test/baseballStats_data.csv]
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Finished building StatsCollector!
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Collected stats for 97889 documents
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Created dictionary for INT column: homeRuns with cardinality: 67, range: 0 to 73
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Created dictionary for INT column: playerStint with cardinality: 5, range: 1 to 5
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Created dictionary for INT column: groundedIntoDoublePlays with cardinality: 35, range: 0 to 36
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Created dictionary for INT column: numberOfGames with cardinality: 165, range: 1 to 165
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Created dictionary for INT column: AtBatting with cardinality: 699, range: 0 to 716
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Created dictionary for INT column: stolenBases with cardinality: 114, range: 0 to 138
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Created dictionary for INT column: tripples with cardinality: 32, range: 0 to 36
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Created dictionary for INT column: hitsByPitch with cardinality: 41, range: 0 to 51
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Created dictionary for STRING column: teamID with cardinality: 149, max length in bytes: 3, range: ALT to WSU
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Created dictionary for INT column: numberOfGamesAsBatter with cardinality: 166, range: 0 to 165
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Created dictionary for INT column: strikeouts with cardinality: 199, range: 0 to 223
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Created dictionary for INT column: sacrificeFlies with cardinality: 20, range: 0 to 19
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Created dictionary for INT column: caughtStealing with cardinality: 36, range: 0 to 42
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Created dictionary for INT column: baseOnBalls with cardinality: 154, range: 0 to 232
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Created dictionary for STRING column: playerName with cardinality: 11976, max length in bytes: 43, range: to Zoilo Casanova
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Created dictionary for INT column: doules with cardinality: 64, range: 0 to 67
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Created dictionary for STRING column: league with cardinality: 7, max length in bytes: 2, range: AA to UA
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Created dictionary for INT column: yearID with cardinality: 143, range: 1871 to 2013
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Created dictionary for INT column: hits with cardinality: 250, range: 0 to 262
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Created dictionary for INT column: runsBattedIn with cardinality: 175, range: 0 to 191
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Created dictionary for INT column: G_old with cardinality: 166, range: 0 to 165
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Created dictionary for INT column: sacrificeHits with cardinality: 54, range: 0 to 67
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Created dictionary for INT column: intentionalWalks with cardinality: 45, range: 0 to 120
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Created dictionary for INT column: runs with cardinality: 167, range: 0 to 192
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Created dictionary for STRING column: playerID with cardinality: 18107, max length in bytes: 9, range: aardsda01 to zwilldu01
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Start building IndexCreator!
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Finished records indexing in IndexCreator!
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Finished segment seal!
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Converting segment: /Users/host1/Desktop/test2/baseballStats_data_0 to v3 format
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v3 segment location for segment: baseballStats_data_0 is /Users/host1/Desktop/test2/baseballStats_data_0/v3
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Deleting files in v1 segment directory: /Users/host1/Desktop/test2/baseballStats_data_0
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Driver, record read time : 369
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Driver, stats collector time : 0
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Driver, indexing time : 373
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Here is an example of executing a query on a Pinot table:
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$ ./pinot-distribution/target/apache-pinot-0.8.0-SNAPSHOT-bin/apache-pinot-0.8.0-SNAPSHOT-bin/bin/pinot-admin.sh PostQuery -query "select count(*) from baseballStats"
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Executing command: PostQuery -brokerHost [broker_host] -brokerPort [broker_port] -query select count(*) from baseballStats
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Result: {"aggregationResults":[{"function":"count_star","value":"97889"}],"exceptions":[],"numServersQueried":1,"numServersResponded":1,"numSegmentsQueried":1,"numSegmentsProcessed":1,"numSegmentsMatched":1,"numDocsScanned":97889,"numEntriesScannedInFilter":0,"numEntriesScannedPostFilter":0,"numGroupsLimitReached":false,"totalDocs":97889,"timeUsedMs":107,"segmentStatistics":[],"traceInfo":{}}
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Monitoring Pinot
Pinot exposes several metrics to monitor the service and ensure that pinot users are not experiencing issues. In this section we discuss some of the key metrics that are useful to monitor. A full list of metrics is available in the Metrics section.
Pinot Server
- Number of missing segments that the broker queried for (expected to be on the server) but the server didn’t have. This can be due to retention or stale routing table.
- Total time to take from receiving to finishing executing the query.
- The number of exception which might have occurred during query execution.
- This gives a binary value based on whether low-level consumption is healthy (1) or unhealthy (0). It’s important to ensure at least a single replica of each partition is consuming.
- The highest offset which has been consumed so far.
Pinot Broker
- The rate which an individual broker is receiving queries. Units are in QPS.
- These multiple metrics will indicate if a query is dropped, ie the processing of that query has been forfeited for some reason.
- Indicates a count of partial responses. A partial response is when at least 1 of the requested servers fails to respond to the query.
- Binary metric which will indicate when the configured QPS quota for a table is exceeded (1) or if there is capacity remaining (0).
- Percentage of the configured QPS quota being utilized.
Pinot Controller
Many of the controller metrics include a table name and thus are dynamically generated in the code. The metrics below point to the classes which generate the corresponding metrics.
To get the real metric name, the easiest route is to spin up a controller instance, create a table with the desired name and look through the generated metrics.
Todo
Give a more detailed explanation of how metrics are generated, how to identify real metrics names and where to find them in the code.
- Percentage of complete online replicas in external view as compared to replicas in ideal state.
- Number of segments in an
ERRORstate for a given table.
- The time in hours since the last time an offline segment has been pushed to the controller.
- Percentage of complete online replicas in external view as compared to replicas in ideal state.
- Shows how much of the table’s storage quota is currently being used, metric will a percentage of a the entire quota.
Last modified 8mo ago