To query using distributed joins, window functions, and other multi-stage operators in real time, you must enable the multi-stage query engine (v2). To enable v2, do any of the following:

• Enable the multi-stage query engine in the Query Console

• Programmatically access the multi-stage query engine:

  • Query using REST APIs

  • Query outside of the APIs using the query option

To learn more about what the multi-stage query engine is, see Multi-stage query engine (v2).

Enable the multi-stage query engine in the Query Console

• To enable the multi-stage query engine, in the Pinot Query Console, select the Use Multi-Stage Engine check box.

Programmatically access the multi-stage query engine

To query the Pinot multi-stage query engine, use REST APIs or the query option:

Use REST APIs

The Controller admin API and the Broker query API allow optional JSON payload for configuration. For example:

• For Controller Admin API

curl -X POST http://localhost:9000/sql -d 
'
{
  "sql": "select * from baseballStats limit 10",
  "trace": false,
  "queryOptions": "useMultistageEngine=true"
}
'

• For Broker Query API

curl -X POST http://localhost:8000/query/sql -d '
{
  "sql": "select * from baseballStats limit 10",
  "trace": false,
  "queryOptions": "useMultistageEngine=true"
}
'

Use the query option

To enable the multi-stage engine via a query outside of the API, add the useMultistageEngine=true option to the top of your query.

For example:

SET useMultistageEngine=true; -- indicator to enable the multi-stage engine.
SELECT * from baseballStats limit 10

Need for special NULL value handling

By default, Pinot transforms null values coming from the data source to a default value determined by the type of the corresponding column (or as specified in the schema). Eg: for INT column, the default will be 0 and for STRING column, the default is "null". This transformation is necessary to ensure all the indices can be built correctly during segment creation. However, we're now unable to keep track of the null values in the Pinot table and hence cannot support queries such as:

There is a workaround by matching with default values in the filter predicate. However, this is error prone since oftentimes it's difficult to distinguish valid values from the default null values. Therefore, we added first class NULL value support in Pinot for overcoming this limitation. As of today, the latest version supports NULL filter predicates only. Generic support for NULL handling in query execution is in progress (eg: within aggregation functions such as count or sum).

High Level Architecture

To turn on NULL handling, simply enable the boolean flag in the table index config called as nullHandlingEnabled (see ). Note - this will cause Pinot to use additional memory and disk space per segment. The details are as follows:

Ingestion Phase

During data ingestion (either real-time/offline) eachGenericRow object derived from the original data source record keeps track of all the column names containing null values. This is done as part of the NullValueTransformer. For each such column, the segment creation logic updates a NULL value vector (implemented by a roaring bitmap) with the corresponding document ID. Effectively, at the end of the segment creation process we get a per column NULL value vector which can give us the set of document IDs containing null values for that column. This per column vector is then exposed through the DataSource interface for use in query execution.

Query Phase

During Query execution, if the query includes a IS NULL or IS NOT NULL predicate as shown above, we fetch the NULL value vector for the corresponding column within FilterPlanNode and retrieve the corresponding bitmap which represents all document IDs containing NULL values for that column. This bitmap is then used to create a BitmapBasedFilterOperatorwhich does the actual filtering operation.

Ingesting Offline data

Segments for offline tables are constructed outside of Pinot, typically in Hadoop via map-reduce jobs and ingested into Pinot via REST API provided by the Controller. Pinot provides libraries to create Pinot segments out of input files in AVRO, JSON or CSV formats in a hadoop job, and push the constructed segments to the controllers via REST APIs.

When an Offline segment is ingested, the controller looks up the table’s configuration and assigns the segment to the servers that host the table. It may assign multiple servers for each segment depending on the number of replicas configured for that table.

Pinot supports different segment assignment strategies that are optimized for various use cases.

Once segments are assigned, Pinot servers get notified via Helix to “host” the segment. The segments are downloaded from the remote segment store to the local storage, untarred, and memory-mapped.

Once the server has loaded (memory-mapped) the segment, Helix notifies brokers of the availability of these segments. The brokers start to include the new segments for queries. Brokers support different routing strategies depending on the type of table, the segment assignment strategy, and the use case.

Data in offline segments are immutable (Rows cannot be added, deleted, or modified). However, segments may be replaced with modified data.

Starting from release-0.11.0, Pinot supports uploading offline segments to real-time tables. This is useful when user wants to bootstrap a real-time table with some initial data, or add some offline data to a real-time table without changing the data stream. Note that this is different from the hybrid table setup, and no time boundary is maintained between the offline segments and the real-time segments.

Ingesting Real-time Data

Segments for real-time tables are constructed by Pinot servers with rows ingested from data streams such as Kafka. Rows ingested from streams are made available for query processing as soon as they are ingested, thus enabling applications such as those that need real-time charts on analytics.

In large scale installations, data in streams is typically split across multiple stream partitions. The underlying stream may provide consumer implementations that allow applications to consume data from any subset of partitions, including all partitions (or, just from one partition).

A pinot table can be configured to consume from streams in one of two modes:

• LowLevel: This is the preferred mode of consumption. Pinot creates independent partition-level consumers for each partition. Depending on the the configured number of replicas, multiple consumers may be created for each partition, taking care that no two replicas exist on the same server host. Therefore you need to provision at least as many hosts as the number of replicas configured.

• HighLevel: Pinot creates one stream-level consumer that consumes from all partitions. Each message consumed could be from any of the partitions of the stream. Depending on the configured number of replicas, multiple stream-level consumers are created, taking care that no two replicas exist on the same server host. Therefore you need to provision exactly as many hosts as the number of replicas configured.

Of course, the underlying stream should support either mode of consumption in order for a Pinot table to use that mode. Kafka has support for both of these modes. See Stream ingestion for more information on the support of other data streams in Pinot.

In either mode, Pinot servers store the ingested rows in volatile memory until either one of the following conditions are met:

1. A certain number of rows are consumed

2. The consumption has gone on for a certain length of time

(See StreamConfigs Section on how to set these values, or have pinot compute them for you)

Upon reaching either one of these limits, the servers do the following:

• Pause consumption

• Persist the rows consumed so far into non-volatile storage

• Continue consuming new rows into volatile memory again.

The persisted rows form what we call a completed segment (as opposed to a consuming segment that resides in volatile memory).

In LowLevel mode, the completed segments are persisted the into local non-volatile store of pinot server as well as the segment store of the pinot cluster (See Pinot Architecture Overview). This allows for easy and automated mechanisms for replacing pinot servers, or expanding capacity, etc. Pinot has special mechanisms that ensure that the completed segment is equivalent across all replicas.

During segment completion, one winner is chosen by the controller from all the replicas as the committer server. The committer server builds the segment and uploads it to the controller. All the other non-committer servers follow one of these two paths:

1. If the in-memory segment is equivalent to the committed segment, the non-committer server also builds the segment locally and replaces the in-memory segment

2. If the in-memory segment is non equivalent to the committed segment, the non-committer server downloads the segment from the controller.

For more details on this protocol, refer to this doc.

In HighLevel mode, the servers persist the consumed rows into local store (and not the segment store). Since consumption of rows can be from any partition, it is not possible to guarantee equivalence of segments across replicas.

See Consuming and Indexing rows in Realtime for details.

Learn how to troubleshoot errors when using the multi-stage query engine (v2), and see multi-stage query engine limitations.

Find instructions on how to enable the multi-stage query engine, or see a high-level overview of how the multi-stage query engine works.

Limitations of the multi-stage query engine

We are continuously improving the v2 multi-stage query engine. A few limitations to call out:

Support for multi value columns is limited

Support for multi value columns is limited to projections, and predicates must use the arrayToMv function. For example, to successfully run the following query:

SELECT count(*), RandomAirports FROM airlineStats GROUP BY RandomAirports

You must include arrayToMv in the query as follows:

SELECT count(*), arrayToMv(RandomAirports) FROM airlineStats GROUP BY arrayToMv(RandomAirports)

Schema and other prefixes are not supported

Schema and other prefixes are not supported in queries. For example, the following queries are not supported:

SELECT* from default.myTable;
SELECT * from schemaName.myTable;

Queries without prefixes are supported:

SELECT * from myTable;

Modifying query behavior based on the cluster config is not supported

Modifying query behavior based on the cluster configuration is not supported. distinctcounthll, distinctcounthllmv, distinctcountrawhll, and `distinctcountrawhllmv` use a different default value of log2mParam in the multi-stage v2 engine. In v2, this value can no longer be configured. Therefore, the following query may produce different results in v1 and v2 engine:

select distinctcounthll(col) from myTable

To ensure v2 returns the same result, specify the log2mParam value in your query:

select distinctcounthll(col, 8) from myTable

Ambiguous reference to a projected column in statement clauses

If a column is repeated more than once in SELECT statement, that column requires disambiguate aliasing. For example, in the following query, the reference to colA is ambiguous whether it's to the first or second projected colA:

SELECT colA, colA, COUNT(*)
FROM myTable GROUP BY colA ORDER BY colA

The solution is to rewrite the query either use aliasing:

SELECT colA AS tmpA, colA as tmpB, COUNT(*) 
FROM myTable GROUP BY tmpA, tmpB ORDER BY tmpA

Or use index-based referencing:

SELECT colA, colA, COUNT(*) 
FROM myTable GROUP BY 1, 2 ORDER BY 1

Tightened restriction on function naming

Pinot single-stage query engine automatically removes the underscore _ character from function names. So co_u_n_t()is equivalent to count().

In v2, function naming restrictions were tightened, so the underscore(_) character is only allowed to separate word boundaries in a function name. Also camel case is supported in function names. For example, the following function names are allowed:

is_distinct_from(...)
isDistinctFrom(...)

Default names for projections with function calls

Default names for projections with function calls are different between v1 and v2.

• For example, in v1, the following query:

  SELECT count(*) from mytable 

Returns the following result:

    "columnNames": [
        "EXPR$0"
      ],

• In v2, the following function:

  SELECT count(*) from mytable

Returns the following result:

      "columnNames": [
        "count(*)"
      ],

Table names and column names are case sensitive

In v2, table and column names and are case sensitive. In v1 they were not. For example, the following two queries are not equivalent in v2:

select * from myTable

select * from mytable

Arbitrary number of arguments isn't supported

An arbitrary number of arguments is no longer supported in v2. For example, in v1, the following query worked:

In v2, this query must be rewritten as follows:

select add(1, add(2,add(3, add(4,5)))) from table

NULL function support

• IS NULL and IS NOT NULL functions do not work correctly in v2

• Using the COUNT function on a NULL column does not work correctly in v2

Troubleshoot errors

Troubleshoot semantic/runtime errors and timeout errors.

Semantic/runtime errors

• Try downloading the latest docker image or building from the latest master commit.

  • We continuously push bug fixes for the multi-stage engine so bugs you encountered might have already been fixed in the latest master build.

• Try rewriting your query.

  • Some functions previously supported in the single-stage query engine (v1) may have a new way to express in the multi-stage engine (v2). Check and see if you are using any non-standard SQL functions or semantics.

Timeout errors

• Try reducing the size of the table(s) used.

  • Add higher selectivity filters to the tables.

• Try executing part of the subquery or a simplified version of the query first.

  • This helps to determine the selectivity and scale of the query being executed.

• Try adding more servers.

  • The new multi-stage engine runs distributed across the entire cluster, so adding more servers to partitioned queries such as GROUP BY aggregates, and equality JOINs help speed up the query runtime.

Many data analytics use-cases only need aggregated data. For example, data used in charts can be aggregated down to one row per time bucket per dimension combination.

Doing this results in much less storage and better query performance. Configuring this for a table is done via the Aggregation Config in the table config.

Aggregation Config

The aggregation config controls the aggregations that happen during real-time data ingestion. Offline aggregations must be handled separately.

Below is a description of the config, which is defined in the ingestion config of the table config.

{
  "tableConfig": {
    "tableName": "...",
    "ingestionConfig": {
      "aggregationConfigs": [{
        "columnName": "aggregatedFieldName",
        "aggregationFunction": "<aggregationFunction>(<originalFieldName>)"
      }]
    }
  }
}

Requirements

The following are required for ingestion aggregation to work:

• Ingestion aggregation config is effective only for real-time tables. (There is no ingestion time aggregation support for offline tables. We need use Merge/Rollup Task or pre-process aggregations in the offline data flow using batch processing engines like Spark/MapReduce).

• Stream ingestion type must be lowLevel.

• All metrics must have aggregation configs.

• All metrics must be noDictionaryColumns.

• aggregatedFieldName must be in the Pinot schema and originalFieldName must not exist in Pinot schema

Example Scenario

Here is an example of sales data, where only the daily sales aggregates per product are needed.

Example Input Data

{"customerID":205,"product_name": "car","price":"1500.00","timestamp":1571900400000}
{"customerID":206,"product_name": "truck","price":"2200.00","timestamp":1571900400000}
{"customerID":207,"product_name": "car","price":"1300.00","timestamp":1571900400000}
{"customerID":208,"product_name": "truck","price":"700.00","timestamp":1572418800000}
{"customerID":209,"product_name": "car","price":"1100.00","timestamp":1572505200000}
{"customerID":210,"product_name": "car","price":"2100.00","timestamp":1572505200000}
{"customerID":211,"product_name": "truck","price":"800.00","timestamp":1572678000000}
{"customerID":212,"product_name": "car","price":"800.00","timestamp":1572678000000}
{"customerID":213,"product_name": "car","price":"1900.00","timestamp":1572678000000}
{"customerID":214,"product_name": "car","price":"1000.00","timestamp":1572678000000}

Schema

Note that the schema only reflects the final table structure.

{
  "schemaName": "daily_sales_schema",
  "dimensionFieldSpecs": [
    {
      "name": "product_name",
      "dataType": "STRING"
    }
  ],
  "metricSpecs": [
    {
      "name": "sales_count",
      "dataType": "LONG"
    },
    {
      "name": "total_sales",
      "dataType": "DOUBLE"
    }
  ],
  "dateTimeFieldSpecs": [
    {
      "name": "daysSinceEpoch",
      "dataType": "LONG",
      "format": "1:MILLISECONDS:EPOCH",
      "granularity": "1:MILLISECONDS"
    }
  ]
}

Table Config

From the below aggregation config example, note that price exists in the input data while total_sales exists in the Pinot Schema.

{
  "tableName": "daily_sales",
  "ingestionConfig": {
    "transformConfigs": [
      {
        "columnName": "daysSinceEpoch",
        "transformFunction": "toEpochDays(timestamp)"
      }
    ],
    "aggregationConfigs": [
      {
        "columnName": "total_sales",
        "aggregationFunction": "SUM(price)"
      },
      {
        "columnName": "sales_count", 
        "aggregationFunction": "COUNT(*)"
      }
    ]
  }
  "tableIndexConfig": {
    "noDictionaryColumns": [
      "sales_count",
      "total_sales"
    ]
  }
}

Example Final Table

Allowed Aggregation Functions

Frequently Asked Questions

Why not use a Startree?

Startrees can only be added to real-time segments after the segments has sealed, and creating startrees is CPU-intensive. Ingestion Aggregation works for consuming segments and uses no additional CPU.

Startrees take additional memory to store, while ingestion aggregation stores less data than the original dataset.

When to not use ingestion aggregation?

If the original rows in non-aggregated form are needed, then ingestion-aggregation cannot be used.

I already use the aggregateMetrics setting?

The aggregateMetrics works the same as Ingestion Aggregation, but only allows for the SUM function.

The current changes are backward compatible, so no need to change your table config unless you need a different aggregation function.

Does this config work for offline data?

Ingestion Aggregation only works for real-time ingestion. For offline data, the offline process needs to generate the aggregates separately.

Why do all metrics need to be aggregated?

If a metric isn't aggregated then it will result in more than one row per unique set of dimensions.

Raw source data often needs to undergo some transformations before it is pushed to Pinot.

Transformations include extracting records from nested objects, applying simple transform functions on certain columns, filtering out unwanted columns, as well as more advanced operations like joining between datasets.

A preprocessing job is usually needed to perform these operations. In streaming data sources you might write a Samza job and create an intermediate topic to store the transformed data.

For simple transformations, this can result in inconsistencies in the batch/stream data source and increase maintenance and operator overhead.

To make things easier, Pinot supports transformations that can be applied via the table config.

Transformation Functions

Pinot supports the following functions:

1. Groovy functions

2. Inbuilt functions

Groovy functions

Groovy functions can be defined using the syntax:

Groovy({groovy script}, argument1, argument2...argumentN)

Any valid Groovy expression can be used.

Enabling Groovy

Allowing execuatable Groovy in ingestion transformation can be a security vulnerability. If you would like to enable Groovy for ingestion, you can set the following controller config.

controller.disable.ingestion.groovy=false

If not set, Groovy for ingestion transformation is disabled by default.

Inbuilt Pinot functions

All the functions defined in this directory annotated with @ScalarFunction (e.g. toEpochSeconds) are supported ingestion transformation functions.

Below are some commonly used built-in Pinot functions for ingestion transformations.

DateTime functions

These functions enable time transformations.

toEpochXXX

Converts from epoch milliseconds to a higher granularity.

toEpochXXXRounded

Converts from epoch milliseconds to another granularity, rounding to the nearest rounding bucket. For example, 1588469352000 (2020-05-01 42:29:12) is 26474489 minutesSinceEpoch. `toEpochMinutesRounded(1588469352000) = 26474480 (2020-05-01 42:20:00)

fromEpochXXX

Converts from an epoch granularity to milliseconds.

Simple date format

Converts simple date format strings to milliseconds and vice-a-versa, as per the provided pattern string.

JSON functions

Types of transformation

Filtering

Records can be filtered as they are being ingested. A filter function can be specified in the filterConfigs in the ingestionConfigs of the table config.

"tableConfig": {
    "tableName": ...,
    "tableType": ...,
    "ingestionConfig": {
        "filterConfig": {
            "filterFunction": "<expression>"
        }
    }
}

If the expression evaluates to true, the record will be filtered out. The expressions can use any of the transform functions described in the previous section.

Consider a table that has a column timestamp. If you want to filter out records that are older than timestamp 1589007600000, you could apply the following function:

"ingestionConfig": {
    "filterConfig": {
        "filterFunction": "Groovy({timestamp < 1589007600000}, timestamp)"
    }
}

Consider a table that has a string column campaign and a multi-value column double column prices. If you want to filter out records where campaign = 'X' or 'Y' and sum of all elements in prices is less than 100, you could apply the following function:

"ingestionConfig": {
    "filterConfig": {
        "filterFunction": "Groovy({(campaign == \"X\" || campaign == \"Y\") && prices.sum() < 100}, prices, campaign)"
    }
}

Filter config also supports SQL-like expression of built-in scalar functions for filtering records (starting v 0.11.0+). Example:

"ingestionConfig": {
    "filterConfig": {
        "filterFunction": "strcmp(campaign, 'X') = 0 OR strcmp(campaign, 'Y') = 0 OR timestamp < 1589007600000"
    }
}

Column Transformation

Transform functions can be defined on columns in the ingestion config of the table config.

{ "tableConfig": {
    "tableName": ...,
    "tableType": ...,
    "ingestionConfig": {
        "transformConfigs": [{
          "columnName": "fieldName",
          "transformFunction": "<expression>"
        }]
    },
    ...
}

For example, imagine that our source data contains the prices and timestamp fields. We want to extract the maximum price and store that in the maxPrices field and convert the timestamp into the number of hours since the epoch and store it in the hoursSinceEpoch field. You can do this by applying the following transformation:

{
"tableName": "myTable",
...
"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "maxPrice",
      "transformFunction": "Groovy({prices.max()}, prices)" // groovy function
    },
    {
      "columnName": "hoursSinceEpoch",
      "transformFunction": "toEpochHours(timestamp)" // built-in function
    }]
  }
}

Below are some examples of commonly used functions.

String concatenation

Concat firstName and lastName to get fullName

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "fullName",
      "transformFunction": "Groovy({firstName+' '+lastName}, firstName, lastName)"
    }]
}

Find an element in an array

Find max value in array bids

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "maxBid",
      "transformFunction": "Groovy({bids.max{ it.toBigDecimal() }}, bids)"
    }]
}

Time transformation

Convert timestamp from MILLISECONDS to HOURS

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "hoursSinceEpoch",
      "transformFunction": "Groovy({timestamp/(1000*60*60)}, timestamp)"
    }]
}

Column name change

Change name of the column from user_id to userId

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "userId",
      "transformFunction": "Groovy({user_id}, user_id)"
    }]
}

Extract value from a column containing space

Pinot doesn't support columns that have spaces, so if a source data column has a space, we'll need to store that value in a column with a supported name. To extract the value from first Name into the column firstName, run the following:

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "firstName",
      "transformFunction": "\"first Name \""
    }]
}

Ternary operation

If eventType is IMPRESSION set impression to 1. Similar for CLICK.

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "impressions",
      "transformFunction": "Groovy({eventType == 'IMPRESSION' ? 1: 0}, eventType)"
    },
    {
      "columnName": "clicks",
      "transformFunction": "Groovy({eventType == 'CLICK' ? 1: 0}, eventType)"
    }]
}

AVRO Map

Store an AVRO Map in Pinot as two multi-value columns. Sort the keys, to maintain the mapping. 1) The keys of the map as map_keys 2) The values of the map as map_values

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "map2_keys",
      "transformFunction": "Groovy({map2.sort()*.key}, map2)"
    },
    {
      "columnName": "map2_values",
      "transformFunction": "Groovy({map2.sort()*.value}, map2)"
    }]
}

Chaining transformations

Transformations can be chained. This means that you can use a field created by a transformation in another transformation function.

For example, we might have the following JSON document in the data field of our source data:

{
  "userId": "12345678__foo__othertext"
}

We can apply one transformation to extract the userId and then another one to pull out the numerical part of the identifier:

"ingestionConfig": {
    "transformConfigs": [
      {
        "columnName": "userOid",
        "transformFunction": "jsonPathString(data, '$.userId')"
      },
      {
        "columnName": "userId",
        "transformFunction": "Groovy({Long.valueOf(userOid.substring(0, 8))}, userOid)"
      }
   ]
}

Flattening

There are 2 kinds of flattening:

One record into many

This is not natively supported as of yet. You can write a custom Decoder/RecordReader if you want to use this. Once the Decoder generates the multiple GenericRows from the provided input record, a List<GenericRow> should be set into the destination GenericRow, with the key $MULTIPLE_RECORDS_KEY$. The segment generation drivers will treat this as a special case and handle the multiple records case.

Extract attributes from complex objects

Feature TBD

Start Pinot components (scripts or docker images)

Set up Pinot by starting each component individually

export PINOT_VERSION=0.10.0
export PINOT_IMAGE=apachepinot/pinot:${PINOT_VERSION}
docker pull ${PINOT_IMAGE}

docker network create -d bridge pinot-demo

docker run \
    --network=pinot-demo \
    --name  pinot-zookeeper \
    --restart always \
    -p 2181:2181 \
    -d zookeeper:3.5.6

docker run \
    --network pinot-demo --name=zkui \
    -p 9090:9090 \
    -e ZK_SERVER=pinot-zookeeper:2181 \
    -d qnib/plain-zkui:latest

docker run \
    --network=pinot-demo \
    --name pinot-controller \
    -p 9000:9000 \
    -d ${PINOT_IMAGE} StartController \
    -zkAddress pinot-zookeeper:2181

docker run \
    --network=pinot-demo \
    --name pinot-broker \
    -d ${PINOT_IMAGE} StartBroker \
    -zkAddress pinot-zookeeper:2181

export PINOT_IMAGE=apachepinot/pinot:0.3.0-SNAPSHOT
docker run \
    --network=pinot-demo \
    --name pinot-server \
    -d ${PINOT_IMAGE} StartServer \
    -zkAddress pinot-zookeeper:2181

docker container ls -a

CONTAINER ID        IMAGE                              COMMAND                  CREATED              STATUS                PORTS                                                  NAMES
9e80c3fcd29b        apachepinot/pinot:0.3.0-SNAPSHOT   "./bin/pinot-admin.s…"   18 seconds ago       Up 17 seconds         8096-8099/tcp, 9000/tcp                                pinot-server
f4c42a5865c7        apachepinot/pinot:0.3.0-SNAPSHOT   "./bin/pinot-admin.s…"   21 seconds ago       Up 21 seconds         8096-8099/tcp, 9000/tcp                                pinot-broker
a413b0013806        apachepinot/pinot:0.3.0-SNAPSHOT   "./bin/pinot-admin.s…"   26 seconds ago       Up 25 seconds         8096-8099/tcp, 0.0.0.0:9000->9000/tcp                  pinot-controller
9d3b9c4d454b        zookeeper:3.5.6                    "/docker-entrypoint.…"   About a minute ago   Up About a minute     2888/tcp, 3888/tcp, 0.0.0.0:2181->2181/tcp, 8080/tcp   pinot-zookeeper

$ export PINOT_VERSION=0.10.0
$ tar -xvf apache-pinot-${PINOT_VERSION}-bin.tar.gz

$ cd apache-pinot-${PINOT_VERSION}-bin
$ ls
DISCLAIMER    LICENSE        NOTICE        bin        conf        lib        licenses    query_console    sample_data

$ PINOT_INSTALL_DIR=`pwd`

cd apache-pinot-${PINOT_VERSION}-bin
bin/pinot-admin.sh StartZookeeper

bin/pinot-admin.sh StartController \
    -zkAddress localhost:2181

bin/pinot-admin.sh StartBroker \
    -zkAddress localhost:2181

bin/pinot-admin.sh StartServer \
    -zkAddress localhost:2181

Start Pinot Using Config Files

Often times we need to customized the setup of Pinot components. Hence user can compile a config file and use it to start Pinot components.

Below are the examples config files and sample command to start Pinot.

Pinot Controller

Below is a sample pinot-controller.conf used in HelmChart setup.

controller.helix.cluster.name=pinot-quickstart
controller.port=9000
controller.vip.host=pinot-controller
controller.vip.port=9000
controller.data.dir=/var/pinot/controller/data
controller.zk.str=pinot-zookeeper:2181
pinot.set.instance.id.to.hostname=true

In order to run Pinot Controller, the command is:

bin/pinot-admin.sh StartController -configFileName config/pinot-controller.conf

Configure Controller

Below are some configurations you can set in Pinot Controller. You can head over to Controller for complete list of available configs.

Pinot Broker

Below is a sample pinot-broker.conf used in HelmChart setup.

pinot.broker.client.queryPort=8099
pinot.broker.routing.table.builder.class=random
pinot.set.instance.id.to.hostname=true

In order to run Pinot Broker, the command is:

bin/pinot-admin.sh StartBroker -clusterName pinot-quickstart -zkAddress pinot-zookeeper:2181 -configFileName config/pinot-broker.conf

Configure Broker

Below are some configurations you can set in Pinot Broker. You can head over to Broker for complete list of available configs.

Pinot Server

Below is a sample pinot-server.conf used in HelmChart setup.

pinot.server.netty.port=8098
pinot.server.adminapi.port=8097
pinot.server.instance.dataDir=/var/pinot/server/data/index
pinot.server.instance.segmentTarDir=/var/pinot/server/data/segment
pinot.set.instance.id.to.hostname=true

In order to run Pinot Server, the command is:

bin/pinot-admin.sh StartServer -clusterName pinot-quickstart -zkAddress pinot-zookeeper:2181 -configFileName config/pinot-server.conf

Configure Server

Below are some outstanding configurations you can set in Pinot Server. You can head over to Server for complete list of available configs.

Create and Configure table

A TABLE in regular database world is represented as <TABLE>_OFFLINE and/or <TABLE>_REALTIME in Pinot depending on the ingestion mode (batch, real-time, hybrid)

See examples for all possible batch/streaming tables.

Batch Table Creation

See Batch Tables for table configuration details and how to customize it.

docker run \
    --network=pinot-demo \
    --name pinot-batch-table-creation \
    ${PINOT_IMAGE} AddTable \
    -schemaFile examples/batch/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/batch/airlineStats/airlineStats_offline_table_config.json \
    -controllerHost pinot-controller \
    -controllerPort 9000 \
    -exec

Executing command: AddTable -tableConfigFile examples/batch/airlineStats/airlineStats_offline_table_config.json -schemaFile examples/batch/airlineStats/airlineStats_schema.json -controllerHost pinot-controller -controllerPort 9000 -exec
Sending request: http://pinot-controller:9000/schemas to controller: a413b0013806, version: Unknown
{"status":"Table airlineStats_OFFLINE succesfully added"}

bin/pinot-admin.sh AddTable \
    -schemaFile examples/batch/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/batch/airlineStats/airlineStats_offline_table_config.json \
    -exec

Streaming Table Creation

See Streaming Tables for table configuration details and how to customize it.

docker run \
    --network pinot-demo --name=kafka \
    -e KAFKA_ZOOKEEPER_CONNECT=pinot-zookeeper:2181/kafka \
    -e KAFKA_BROKER_ID=0 \
    -e KAFKA_ADVERTISED_HOST_NAME=kafka \
    -d wurstmeister/kafka:latest

docker exec \
  -t kafka \
  /opt/kafka/bin/kafka-topics.sh \
  --zookeeper pinot-zookeeper:2181/kafka \
  --partitions=1 --replication-factor=1 \
  --create --topic flights-realtime

docker run \
    --network=pinot-demo \
    --name pinot-streaming-table-creation \
    ${PINOT_IMAGE} AddTable \
    -schemaFile examples/stream/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/docker/table-configs/airlineStats_realtime_table_config.json \
    -controllerHost pinot-controller \
    -controllerPort 9000 \
    -exec

Executing command: AddTable -tableConfigFile examples/docker/table-configs/airlineStats_realtime_table_config.json -schemaFile examples/stream/airlineStats/airlineStats_schema.json -controllerHost pinot-controller -controllerPort 9000 -exec
Sending request: http://pinot-controller:9000/schemas to controller: 8fbe601012f3, version: Unknown
{"status":"Table airlineStats_REALTIME succesfully added"}

bin/pinot-admin.sh StartZookeeper -zkPort 2191

bin/pinot-admin.sh  StartKafka -zkAddress=localhost:2191/kafka -port 19092

bin/pinot-admin.sh AddTable \
    -schemaFile examples/stream/airlineStats/airlineStats_schema.json \
    -tableConfigFile examples/stream/airlineStats/airlineStats_realtime_table_config.json \
    -exec

Load Data

Now that the table is configured, let's load some data. Data can be loaded in batch mode or streaming mode. See ingestion overview page for details. Loading data involves generating pinot segments from raw data and pushing them to the pinot cluster.

Load Data in Batch

User can always generate and push segments to Pinot via standalone scripts or using frameworks such as Hadoop or Spark. See this page for more details on setting up Data Ingestion Jobs.

Below example goes with the standalone mode.

docker run \
    --network=pinot-demo \
    --name pinot-data-ingestion-job \
    ${PINOT_IMAGE} LaunchDataIngestionJob \
    -jobSpecFile examples/docker/ingestion-job-specs/airlineStats.yaml

SegmentGenerationJobSpec:
!!org.apache.pinot.spi.ingestion.batch.spec.SegmentGenerationJobSpec
excludeFileNamePattern: null
executionFrameworkSpec: {extraConfigs: null, name: standalone, segmentGenerationJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner,
  segmentTarPushJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner,
  segmentUriPushJobRunnerClassName: org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner}
includeFileNamePattern: glob:**/*.avro
inputDirURI: examples/batch/airlineStats/rawdata
jobType: SegmentCreationAndTarPush
outputDirURI: examples/batch/airlineStats/segments
overwriteOutput: true
pinotClusterSpecs:
- {controllerURI: 'http://pinot-controller:9000'}
pinotFSSpecs:
- {className: org.apache.pinot.spi.filesystem.LocalPinotFS, configs: null, scheme: file}
pushJobSpec: {pushAttempts: 2, pushParallelism: 1, pushRetryIntervalMillis: 1000,
  segmentUriPrefix: null, segmentUriSuffix: null}
recordReaderSpec: {className: org.apache.pinot.plugin.inputformat.avro.AvroRecordReader,
  configClassName: null, configs: null, dataFormat: avro}
segmentNameGeneratorSpec: null
tableSpec: {schemaURI: 'http://pinot-controller:9000/tables/airlineStats/schema',
  tableConfigURI: 'http://pinot-controller:9000/tables/airlineStats', tableName: airlineStats}

Trying to create instance for class org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner
Initializing PinotFS for scheme file, classname org.apache.pinot.spi.filesystem.LocalPinotFS
Finished building StatsCollector!
Collected stats for 403 documents
Created dictionary for INT column: FlightNum with cardinality: 386, range: 14 to 7389
Using fixed bytes value dictionary for column: Origin, size: 294
Created dictionary for STRING column: Origin with cardinality: 98, max length in bytes: 3, range: ABQ to VPS
Created dictionary for INT column: Quarter with cardinality: 1, range: 1 to 1
Created dictionary for INT column: LateAircraftDelay with cardinality: 50, range: -2147483648 to 303
......
......
Pushing segment: airlineStats_OFFLINE_16085_16085_29 to location: http://pinot-controller:9000 for table airlineStats
Sending request: http://pinot-controller:9000/v2/segments?tableName=airlineStats to controller: a413b0013806, version: Unknown
Response for pushing table airlineStats segment airlineStats_OFFLINE_16085_16085_29 to location http://pinot-controller:9000 - 200: {"status":"Successfully uploaded segment: airlineStats_OFFLINE_16085_16085_29 of table: airlineStats"}
Pushing segment: airlineStats_OFFLINE_16084_16084_30 to location: http://pinot-controller:9000 for table airlineStats
Sending request: http://pinot-controller:9000/v2/segments?tableName=airlineStats to controller: a413b0013806, version: Unknown
Response for pushing table airlineStats segment airlineStats_OFFLINE_16084_16084_30 to location http://pinot-controller:9000 - 200: {"status":"Successfully uploaded segment: airlineStats_OFFLINE_16084_16084_30 of table: airlineStats"}

bin/pinot-admin.sh LaunchDataIngestionJob \
    -jobSpecFile examples/batch/airlineStats/ingestionJobSpec.yaml

JobSpec yaml file has all the information regarding data format, input data location and pinot cluster coordinates. Note that this assumes that the controller is RUNNING to fetch the table config and schema. If not, you will have to configure the spec to point at their location. See Pinot Ingestion Job for more details.

Load Data in Streaming

Kafka

docker run \
  --network pinot-demo \
  --name=loading-airlineStats-data-to-kafka \
  ${PINOT_IMAGE} StreamAvroIntoKafka \
  -avroFile examples/stream/airlineStats/sample_data/airlineStats_data.avro \
  -kafkaTopic flights-realtime -kafkaBrokerList kafka:9092 -zkAddress pinot-zookeeper:2181/kafka

bin/pinot-admin.sh StreamAvroIntoKafka \
  -avroFile examples/stream/airlineStats/sample_data/airlineStats_data.avro \
  -kafkaTopic flights-realtime -kafkaBrokerList localhost:19092 -zkAddress localhost:2191/kafka