StarRocks vs Apache Pinot

Compare and contrast StarRocks and Apache Pinot by architecture, ingestion, queries, performance, and scalability.

Compare StarRocks to Rockset here

Compare Apache Pinot to Rockset here

StarRocks vs Apache Pinot Architecture

Architecture

StarRocks

Apache Pinot

Deployment model

PaaS or self managed

Use of storage hierarchy

Data is stored on disk and in memory

Hot storage plus Deep Store for backup and restore operations

Isolation of ingest and query

No, but you can limit resources for ingestion and querying separately

Separation of compute and storage

No, but StarRocks supports nodes that don't store data locally

No, although StarTree separates compute and cloud storage, which is an important consideration for those considering StarTree vs. open-source Pinot

Isolation for multiple applications

Full isolation with replication

StarRocks is a high-performance OLAP database that can be deployed on the cloud or self managed. StarRocks does not separate compute and storage and offers limited options for resource isolation. It offers a robust set of features and high performance but requires considerable expertise to operate and scale.

Pinot is a real-time distributed OLAP datastore that ingests both batch and streaming data. It has a distributed systems architecture that scales both horizontally and vertically, but unlike alternative OLAP databases, it does not decouple storage and compute. It supports both self-managed and PaaS options.

StarRocks vs Apache Pinot Ingestion

Ingestion

StarRocks

Apache Pinot

Data sources

Streaming • Kafka • Flink Data lakes • HDFS compatible • Cloud storage

Streaming • AWS Kinesis • Apache Kafka Batch • Cloud Storage • PostgreSQL • MySQL • Snowflake • Google BigQuery File upload Write API

Semi structured data

Supports columns with JSON data • Does not support mixed-type columns • Support for star and snowflake schemas

Support for JSON column types, but JSON must first be converted to a string

Transformations and rollups

Yes, via materialized views

Yes

StarRocks ingests data from a variety of sources, including both batch and streaming data. StarRocks can ingest nested JSON data, but enforces type at the column level.

Pinot supports high-performance ingest from streaming data sources. Each table is either offline or real time. Real-time tables have a smaller retention period and scale based on ingestion rate while offline tables have a larger retention period and scale based on the amount of data. In order to persistently store the generated segments that make up a table, you will need to change controller and server configs to add deep storage.

StarRocks vs Apache Pinot Performance

Performance

StarRocks

Apache Pinot

Updates

While StarRocks is mutable, the update rate is slow, which is why it is most often used for append-only workloads

• By default, all data in Pinot is immutable • Upserts only supported for streaming ingest • No support for upserts on data using the star tree index • No support for upserts on out-of-order events

Indexing

Columnar index, limited support for inverted indexes

• Manually configured • Forward index, inverted Index, Star-Tree index (columnar), bloom filter, range index, search index, JSON index, geospatial index, timestamp index

Query latency

50-1000ms queries on 100s of TB

Storage format

• StarRocks is a columnstore that organizes data into prefix indexes, per-column data blocks, and per-column indexes • All data is replicated 3 times to achieve both fault-tolerance and concurrency

Pinot stores data in a columnar format and adds additional indexes to perform fast filtering, aggregation and group by

Streaming ingest

Data latency is typically 1-2 seconds

1-2 second ingest for streaming data

StarRocks was purpose-built for high-performance ingest, low-latency queries, and high concurrency. Optimized performance requires significant manual tuning.

Like its competitors, Pinot can achieve sub-second query latency at high concurrency. However, this level of performance requires tuning, management, and deep expertise. Compared with the open-source version, the PaaS versions of Pinot address some of these issues, but similarly require expertise while making tradeoffs affecting query performance.

StarRocks vs Apache Pinot Queries

Queries

StarRocks

Apache Pinot

Joins

Multi-table join support

SQL joins limited to inner join, left-outer, and semi-join

Query language

SQL

Developer tooling

Minimal

• API for querying SQL via POST command • Clients for JDBC, Java, Python, and Golang • Integrations with Trino and Presto

Visualization tools

Compatibility with MySQL protocols enables StarRocks to work with BI tools

Integrations with Tableau and Superset

StarRocks uses a high-performance vectorized SQL engine, a custom-built cost-based optimizer, and has support for materialized views.

In Pinot, SQL-like queries are received by brokers and scatter the request between real-time and offline servers. The two tables then process requests, send results back to the broker, and responds with the result. Joins are limited, as is support for UDFs and subqueries, making Pinot more or less useful depending on the use case.

StarRocks vs Apache Pinot Scalability

Scalability

StarRocks

Apache Pinot

Vertical scaling

• Both frontend and backend nodes can be manually resized

All 4 pinot node types can be resized manually

Horizontal scaling

• Both frontend and backend nodes can be manually scaled horizontally

• Users can add additional Pinot nodes to scale horizontally • Rebalancing is manual

StarRocks can scale up or out, but its tightly coupled compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling StarRocks often requires deep expertise as there are many levels of the system that need to be managed.

Pinot allows for vertical scaling by increasing CPU and memory for each node as well as horizontal scaling by adding additional nodes. Capacity planning is a time-consuming, iterative, and manual task. It involves load testing and tuning across multiple vectors including read QPS, write QPS, number of streaming partitions, daily data size, retention period, types of workloads, number and type of segments, and much more.