Each table in Pinot is associated with a schema. A schema defines what fields are present in the table along with the data types.

The schema is stored in Zookeeper along with the table configuration.

Categories

A schema also defines what category a column belongs to. Columns in a Pinot table can be categorized into three categories:

Pinot does not enforce strict rules on which of these categories columns belong to, rather the categories can be thought of as hints to Pinot to do internal optimizations.

For example, metrics may be stored without a dictionary and can have a different default null value.

The categories are also relevant when doing segment merge and rollups. Pinot uses the dimension and time fields to identify records against which to apply merge/rollups.

Metrics aggregation is another example where Pinot uses dimensions and time are used as the key, and automatically aggregates values for the metric columns.

For configuration details, see .

Date and time fields

Since Pinot doesn't have a dedicated DATETIME datatype support, you need to input time in either STRING, LONG, or INT format. However, Pinot needs to convert the date into an understandable format such as epoch timestamp to do operations. You can refer to for more details on supported formats.

Creating a schema

First, Make sure your and running.

Let's create a schema and put it in a JSON file. For this example, we have created a schema for flight data.

Then, we can upload the sample schema provided above using either a Bash command or REST API call.

Check out the schema in the to make sure it was successfully uploaded

A table is a logical abstraction that represents a collection of related data. It is composed of columns and rows (known as documents in Pinot). The columns, data types, and other metadata related to the table are defined using a schema.

Pinot breaks a table into multiple segments and stores these segments in a deep-store such as Hadoop Distributed File System (HDFS) as well as Pinot servers.

In the Pinot cluster, a table is modeled as a Helix resource and each segment of a table is modeled as a Helix Partition.

Pinot supports the following types of tables:

is used to define the table properties, such as name, type, indexing, routing, and retention. It is written in JSON format and is stored in Zookeeper, along with the table schema.

Use the following properties to make your tables faster or leaner:

• Segment

• Indexing

• Tenants

Segments

A table is comprised of small chunks of data known as segments. Learn more about how Pinot creates and manages segments .

For offline tables, segments are built outside of Pinot and uploaded using a distributed executor such as Spark or Hadoop. For details, see .

For real-time tables, segments are built in a specific interval inside Pinot. You can tune the following for the real-time segments.

Flush

The Pinot real-time consumer ingests the data, creates the segment, and then flushes the in-memory segment to disk. Pinot allows you to configure when to flush the segment in the following ways:

• Number of consumed rows: After consuming the specified number of rows from the stream, Pinot will persist the segment to disk.

• Number of rows per segment: Pinot learns and then estimates the number of rows that need to be consumed. The learning phase starts by setting the number of rows to 100,000 (this value can be changed) and adjusts it to reach the appropriate segment size. Because Pinot corrects the estimate as it goes along, the segment size might go significantly over the correct size during the learning phase. You should set this value to optimize the performance of queries.

Replicas A segment can have multiple replicas to provide higher availability. You can configure the number of replicas for a table segment .

Completion Mode By default, if the in-memory segment in the is equivalent to the committed segment, then the non-winner server builds and replaces the segment. If the available segment is not equivalent to the committed segment, the server just downloads the committed segment from the controller.

However, in certain scenarios, the segment build can get very memory-intensive. In these cases, you might want to enforce the non-committer servers to just download the segment from the controller instead of building it again. You can do this by setting completionMode: "DOWNLOAD" in the table configuration.

For details, see .

Download Scheme

A Pinot server might fail to download segments from the deep store, such as HDFS, after its completion. However, you can configure servers to download these segments from peer servers instead of the deep store. Currently, only HTTP and HTTPS download schemes are supported. More methods, such as gRPC/Thrift, are planned be added in the future.

For more details about peer segment download during real-time ingestion, refer to this design doc on

Indexing

You can create multiple indices on a table to increase the performance of the queries. The following types of indices are supported:

• • Dictionary-encoded forward index with bit compression

  • Raw value forward index

For more details on each indexing mechanism and corresponding configurations, see .

Set up on columns to make queries faster. You can also keep segments in off-heap instead of on-heap memory for faster queries.

Pre-aggregation

Aggregate the real-time stream data as it is consumed to reduce segment sizes. We add the metric column values of all rows that have the same values for all dimension and time columns and create a single row in the segment. This feature is only available on REALTIME tables.

The only supported aggregation is SUM. The columns to pre-aggregate need to satisfy the following requirements:

• All metrics should be listed in noDictionaryColumns.

• No multi-value dimensions

• All dimension columns are treated to have a dictionary, even if they appear as noDictionaryColumns in the config.

The following table config snippet shows an example of enabling pre-aggregation during real-time ingestion:

Tenants

Each table is associated with a tenant. A segment resides on the server, which has the same tenant as itself. For details, see .

Optionally, override if a table should move to a server with different tenant based on segment status. The example below adds a tagOverrideConfig under the tenants section for real-time tables to override tags for consuming and completed segments.

In the above example, the consuming segments will still be assigned to serverTenantName_REALTIME hosts, but once they are completed, the segments will be moved to serverTeantnName_OFFLINE.

You can specify the full name of any tag in this section. For example, you could decide that completed segments for this table should be in Pinot servers tagged as allTables_COMPLETED). To learn more about, see the section.

Hybrid table

A hybrid table is a table composed of two tables, one offline and one real-time, that share the same name. In a hybrid table, offline segments can be pushed periodically. The retention on the offline table can be set to a high value because segments are coming in on a periodic basis, whereas the retention on the real-time part can be small.

Once an offline segment is pushed to cover a recent time period, the brokers automatically switch to using the offline table for segments for that time period and use the real-time table only for data not available in the offline table.

To learn how time boundaries work for hybrid tables, see .

A typical use case for hybrid tables is pushing deduplicated, cleaned-up data into an offline table every day while consuming real-time data as it arrives. Data can remain in offline tables for as long as a few years, while the real-time data would be cleaned every few days.

Examples

Create a table config for your data, or see for all possible batch/streaming tables.

Prerequisites

Offline table creation

Check out the table config in the to make sure it was successfully uploaded.

Streaming table creation

Check out the table config in the to make sure it was successfully uploaded.

Hybrid table creation

To create a hybrid table, you have to create the offline and real-time tables individually. You don't need to create a separate hybrid table.

The deep store (or deep storage) is the permanent store for files.

It is used for backup and restore operations. New nodes in a cluster will pull down a copy of segment files from the deep store. If the local segment files on a server gets damaged in some way (or accidentally deleted), a new copy will be pulled down from the deep store on server restart.

The deep store stores a compressed version of the segment files and it typically won't include any indexes. These compressed files can be stored on a local file system or on a variety of other file systems. For more details on supported file systems, see .

Note: Deep store by itself is not sufficient for restore operations. Pinot stores metadata such as table config, schema, segment metadata in Zookeeper. For restore operations, both Deep Store as well as Zookeeper metadata are required.

Pinot has the concept of a table, which is a logical abstraction to refer to a collection of related data. Pinot has a distributed architecture and scales horizontally. Pinot expects the size of a table to grow infinitely over time. In order to achieve this, the entire data needs to be distributed across multiple nodes.

Pinot achieves this by breaking the data into smaller chunks known as segments (similar to shards/partitions in relational databases). Segments can be seen as time-based partitions.

A segment is a horizontal shard representing a chunk of table data with some number of rows. The segment stores data for all columns of the table. Each segment packs the data in a columnar fashion, along with the dictionaries and indices for the columns. The segment is laid out in a columnar format so that it can be directly mapped into memory for serving queries.

Columns can be single or multi-valued and the following types are supported: STRING, BOOLEAN, INT, LONG, FLOAT, DOUBLE, TIMESTAMP or BYTES. Only single-valued BIG_DECIMAL data type is supported.

Columns may be declared to be metric or dimension (or specifically as a time dimension) in the schema. Columns can have default null values. For example, the default null value of a integer column can be 0. The default value for bytes columns must be hex-encoded before it's added to the schema.

Pinot uses dictionary encoding to store values as a dictionary ID. Columns may be configured to be “no-dictionary” column in which case raw values are stored. Dictionary IDs are encoded using minimum number of bits for efficient storage (e.g. a column with a cardinality of 3 will use only 2 bits for each dictionary ID).

A forward index is built for each column and compressed for efficient memory use. In addition, you can optionally configure inverted indices for any set of columns. Inverted indices take up more storage, but improve query performance. Specialized indexes like Star-Tree index are also supported. For more details, see .

Creating a segment

Once the table is configured, we can load some data. Loading data involves generating pinot segments from raw data and pushing them to the pinot cluster. Data can be loaded in batch mode or streaming mode. For more details, see the page.

Load data in batch

Prerequisites

Below are instructions to generate and push segments to Pinot via standalone scripts. For a production setup, you should use frameworks such as Hadoop or Spark. For more details on setting up data ingestion jobs, see

Job Spec YAML

To generate a segment, we need to first create a job spec YAML file. This file contains 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. For full configurations, see .

Create and push segment

To create and push the segment in one go, use the following:

Alternately, you can separately create and then push, by changing the jobType to SegmentCreation or SegmenTarPush.

Templating Ingestion Job Spec

The Ingestion job spec supports templating with Groovy Syntax.

This is convenient if you want to generate one ingestion job template file and schedule it on a daily basis with extra parameters updated daily.

e.g. you could set inputDirURI with parameters to indicate the date, so that the ingestion job only processes the data for a particular date. Below is an example that templates the date for input and output directories.

You can pass in arguments containing values for ${year}, ${month}, ${day} when kicking off the ingestion job: -values $param=value1 $param2=value2...

This ingestion job only generates segments for date 2014-01-03

Load data in streaming

Prerequisites

Below is an example of how to publish sample data to your stream. As soon as data is available to the real-time stream, it starts getting consumed by the real-time servers.

Kafka