There are multiple options for importing data into Pinot. These guides are ready-made examples that show you step-by-step instructions for importing records into Pinot, supported by our plugin architecture.

These guides are meant to get you up and running with imported data as quick as possible. Pinot supports multiple file input formats without needing to change anything other than the file name. Each example imports a ready-made dataset so you can see how things work without needing to bring your own dataset.

Pinot Batch Ingestion

Pinot Stream Ingestion

This guide will show you how to import data using stream ingestion from Apache Kafka topics.

This guide will show you how to import data using stream ingestion with upsert.

Pinot File Systems

By default, Pinot does not come with a storage layer, so all the data sent, won't be stored in case of system crash. In order to persistently store the generated segments, you will need to change controller and server configs to add a deep storage. Checkout File systems for all the info and related configs.

These guides will show you how to import data as well as persist it in the file systems.

Pinot Input Formats

These guides will show you how to import data from a Pinot supported input format.

This guide will show you how to handle the complex type in the ingested data, such as map and array.

Introduction

Pinot batch ingestion involves two parts: routing ingestion job(hourly/daily) and backfill. Here are some tutorials on how routine batch ingestion works in Pinot Offline Table:

• Batch Ingestion Overview

• Batch Ingestion in Practice

High Level Idea

1. Organize raw data into buckets (eg: /var/pinot/airlineStats/rawdata/2014/01/01). Each bucket typically contains several files (eg: /var/pinot/airlineStats/rawdata/2014/01/01/airlineStats_data_2014-01-01_0.avro)

2. Run a Pinot batch ingestion job, which points to a specific date folder like ‘/var/pinot/airlineStats/rawdata/2014/01/01’. The segment generation job will convert each such avro file into a Pinot segment for that day and give it a unique name.

3. Run Pinot segment push job to upload those segments with those uniques names via a Controller API

This newly uploaded data can now be queried in Pinot. However, sometimes users will make changes to the raw data which need to be reflected in Pinot. This process is known as 'Backfill'.

How to Backfill data in Pinot

Pinot supports data modification only at the segment level, which means we should update entire segments for doing backfills. The high level idea is to repeat steps 2 (segment generation) and 3 (segment upload) mentioned above:

• Backfill jobs must run at the same granularity as the daily job. E.g., if you need to backfill data for 2014/01/01, specify that input folder for your backfill job (e.g.: ‘/var/pinot/airlineStats/rawdata/2014/01/01’)

• The backfill job will then generate segments with the same name as the original job (with the new data).

• When uploading those segments to Pinot, the controller will replace the old segments with the new ones (segment names act like primary keys within Pinot) one by one.

Edge case

Backfill jobs expect the same number of (or more) data files on the backfill date. So the segment generation job will create the same number of (or more) segments than the original run.

E.g. assuming table airlineStats has 2 segments(airlineStats_2014-01-01_2014-01-01_0, airlineStats_2014-01-01_2014-01-01_1) on date 2014/01/01 and the backfill input directory contains only 1 input file. Then the segment generation job will create just one segment: airlineStats_2014-01-01_2014-01-01_0. After the segment push job, only segment airlineStats_2014-01-01_2014-01-01_0 got replaced and stale data in segment airlineStats_2014-01-01_2014-01-01_1 are still there.

In case the raw data is modified in such a way that the original time bucket has fewer input files than the first ingestion run, backfill will fail.