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 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 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 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 ). This allows for easy and automated mechanisms for replacing pinot servers, or expanding capacity, etc. Pinot has 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 .

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 for details.

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.

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 or pre-process aggregations in the offline data flow using batch processing engines like Spark/MapReduce).

• type must be lowLevel.

• All metrics must have aggregation configs.

Example Scenario

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

Example Input Data

Schema

Note that the schema only reflects the final table structure.

Table Config

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

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 .

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

  • Query outside of the APIs

To learn more about what the multi-stage query engine is, see .

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:

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:

Null handling is defined in two different parts: at ingestion and at query time.

• means that you have enabled null handling at ingestion.

• means that you have also enabled null handling at query time.

Basic null handling support

By default, null handling is disabled (nullHandlingEnabled=false) in the Table index configuration (). When null support is disabled, IS NOT NULL evaluates to true, and IS NULL evaluates to false. For example, the predicate in the query below matches all records.

Enable basic null support

To enable basic null support (IS NULL and IS NOT NULL) and generate the null index, in the Table index configuration (), set nullHandlingEnabled=true.

When null support is enabled, IS NOT NULL and IS NULL evaluate to true or false according to whether a null is detected.

Example workarounds to handle null values

If you're not able to generate the null index for your use case, you may filter for null values using a default value specified in your schema or a specific value included in your query.

Filter for default null value(s) specified in your schema

1. Specify a default null value (defaultNullValue) in your for dimension fields, (dimensionFieldSpecs), metric fields (metricFieldSpecs), and date time fields (dateTimeFieldSpecs).

2. To filter out the specified default null value, for example, you could write a query like the following:

Filter for a specific value in your query

Filter for a specific value in your query that will not be included in the dataset. For example, to calculate the average age, use -1 to indicate the value of Age is null.

• Rewrite the following query:

• To cover null values as follows:

Advanced null handling support

Under development to improve performance for advanced null handling.

Pinot provides advanced null handling support similar to standard SQL null handling. Because this feature carries a notable performance impact (even queries without null values), this feature is not enabled by default. For optimal query latency, we recommend .

Enable advanced null handling

To enable NULL handling, do the following:

1. To enable null handling during ingestion, in , set**nullHandlingEnabled=true**.

2. To enable null handling for queries, set the**enableNullHandling** .

Ingestion time

To store the null values in a segment, you must enable the nullHandlingEnabled in before ingesting the data.

During real-time or offline ingestion, Pinot checks to see if null handling is enabled, and stores null values in the segment itself. Data ingested when null handling is disabled does not store null values, and should be ingested again.

The nullHandlingEnabled configuration affects all columns in a Pinot table.

Query time

By default, null usage in the predicate is disabled.

For handling nulls in aggregation functions, explicitly enable the null support by setting the query option enableNullHandling to true. Configure this option in one of the following ways:

1. Set enableNullHandling=true at the beginning of the query.

2. If using JDBC, set the connection option enableNullHandling=true (either in the URL or as a property).

When this option is enabled, the Pinot query engine uses a different execution path that checks null predicates. Therefore, some indexes may not be usable, and the query is significantly more expensive. This is the main reason why null handling is not enabled by default.

If the query includes a IS NULL or IS NOT NULL predicate, Pinot fetches the NULL value vector for the corresponding column within FilterPlanNode and retrieves the corresponding bitmap that represents all document IDs containing NULL values for that column. This bitmap is then used to create a BitmapBasedFilterOperator to do the filtering operation.

Examples queries

Select Query

Filter Query

Aggregate Query

Aggregate Filter Query

Group By Query

Order By Query

Transform Query

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

Find instructions on , or see a high-level overview of .

Limitations of the multi-stage query engine

Start Pinot components (scripts or docker images)

Set up Pinot by starting each component individually

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.

In order to run Pinot Controller, the command is:

Configure Controller

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

Pinot Broker

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

In order to run Pinot Broker, the command is:

Configure Broker

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

Pinot Server

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

In order to run Pinot Server, the command is:

Configure Server

Below are some outstanding configurations you can set in Pinot Server. You can head over to 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 for all possible batch/streaming tables.

Batch Table Creation

See for table configuration details and how to customize it.

Automatically add an inverted index to your batch table

By default, the inverted index type is the only type of index that isn't created automatically during segment generation. Instead, they are generated when the segments are loaded on the server. But, waiting to build indexes until load time increases the startup time and takes up resources with every new segment push, which increases the time for other operations such as rebalance.

To automatically create an inverted index during segment generation, add an entry to your in the table configuration file.

This setting works with .

When set to true, Pinot creates an inverted index for the columns that you specify in the invertedIndexColumns list in the table configuration.

This setting is false by default.

Set createInvertedIndexDuringSegmentGeneration to true in your table config, as follows:

When you update this setting in your table configuration, you must to apply the inverted index to all existing segments.

Streaming Table Creation

See for table configuration details and how to customize it.

Use sortedColumn with streaming tables

For tables, you can use a sorted index with sortedColumn to sort data when generating segments as the segment is created. See for more information.

A sorted forward index can be used as an inverted index with better performance, but with the limitation that the search is only applied to one column per table. See to learn more.

Load Data

Now that the table is configured, let's load some data. Data can be loaded in batch mode or streaming mode. See 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 for more details on setting up Data Ingestion Jobs.

Below example goes with the standalone mode.

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 for more details.

Load Data in Streaming

Kafka