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SingleStore vs Apache Pinot

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

Compare SingleStore to Rockset here

Compare Apache Pinot to Rockset here

SingleStore vs Apache Pinot Architecture

Architecture
SingleStore
Apache Pinot
Deployment model
Self managed and SaaS deployment options
PaaS or self managed
Use of storage hierarchy
• Memory - for data requiring the highest performance • High-performance block storage for persistent cache - the working dataset should fit within the persistent cache • Cloud object storage for long-term retention
Hot storage plus Deep Store for backup and restore operations
Isolation of ingest and query
No - databases share ingest and queries
No
Separation of compute and storage
Yes - Singlestore Cloud uses cloud object storage for separation of compute and storage
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
No
Full isolation with replication

SingleStore is a proprietary distributed relational database that handles both transactional and analytical workloads. It relies on memory and a persistent cache to deliver low latency queries. For longer term data retention, SingleStore Cloud separates compute from cloud object storage. SingleStore Cloud pricing is based on compute and storage usage.

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.


SingleStore vs Apache Pinot Ingestion

Ingestion
SingleStore
Apache Pinot
Data sources
Integrations to: Amazon S3, Apache Beam, GCS, HDFS, Kafka, Spark, Qlik Replicate, HVR
Streaming • AWS Kinesis • Apache Kafka Batch • Cloud Storage • PostgreSQL • MySQL • Snowflake • Google BigQuery File upload Write API
Semi structured data
Ingests JSON as a JSON column type
Support for JSON column types, but JSON must first be converted to a string
Transformations and rollups
SingleStore pipelines do common data shaping including normalizing and denormalizing data, adding computed columns, filtering data, mapping data, splitting records into multiple destination tables
Yes

SingleStore has integrations to common data lakes and streams. With SingleStore pipelines, users can perform common data transformations during the ingestion process. SingleStore provides limited support for semi-structured data with its JSON column type. Many users structure data prior to ingestion for optimal query performance.

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.

SingleStore vs Apache Pinot Performance

Performance
SingleStore
Apache Pinot
Updates
SingleStore columnar store/universal storage is immutable. Updates are fast when the data still resides in memory
• 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
Indexes can be manually configured: Skiplist index, hash index, full-text index, geospatial index
• 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 when the working set is contained in memory
50-1000ms queries on 100s of TB
Storage format
Two table formats-either use the rowstore or columnstore/universal storage
Pinot stores data in a columnar format and adds additional indexes to perform fast filtering, aggregation and group by
Streaming ingest
• Columnnar store/universal storage ingests on a batch basis • Data latency is typically seconds by relying on memory
1-2 second ingest for streaming data

SingleStore has two storage formats: a rowstore and a columnar store referred to as universal storage. The columnar store is used for analytical workloads, loading data in batch and relying on memory to achieve seconds of data latency. The columnar store can also execute queries in seconds when the working set is contained in memory. SingleStore provides the ability to configure and manage additional indexes on the data for faster performance.

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.


SingleStore vs Apache Pinot Queries

Queries
SingleStore
Apache Pinot
Joins
Yes
SQL joins limited to inner join, left-outer, and semi-join
Query language
SQL
SQL
Developer tooling
• API for querying data via POST command • JDBC driver, Python client • Compatibility with MySQL and MariaDB to support additional drivers
• API for querying SQL via POST command • Clients for JDBC, Java, Python, and Golang • Integrations with Trino and Presto
Visualization tools
Integrations with Cognos Analytics, Dremio, Looker, Microstrategy, Power BI, Sisense, Tableau and Tibco Spotfire
Integrations with Tableau and Superset

SingleStore supports SQL as its native query language and can perform SQL joins. It is designed for querying structured data with static schemas. Users can create data APIs to execute SQL statements against the database over an HTTP connection. Common SingleStore use cases include business intelligence and analytics, and the database offers a number of integrations to visualization tools.

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.


SingleStore vs Apache Pinot Scalability

Scalability
SingleStore
Apache Pinot
Vertical scaling
• Cloud offering: Resize compute workspaces in the UI or using the Management API • Self-managed offering: Change cluster configuration by updating command-line arguments or to the cluster directly.
All 4 pinot node types can be resized manually
Horizontal scaling
Self-managed offering: Increase or decrease the number of nodes in the cluster. Rebalancing required
• Users can add additional Pinot nodes to scale horizontally • Rebalancing is manual

SingleStore Cloud can be sized up or down using the UI or the Management API. There is no ability to scale out by increasing or decreasing the leaf and aggregator nodes in the cloud offering. In the self-managed offering, horizontal and vertical scaling can occur by updating command-line arguments or the cluster directly. Horizontal scaling does require rebalancing

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.