Apache Druid vs Rockset

Compare and contrast Apache Druid and Rockset by architecture, ingestion, queries, performance, and scalability.

Apache Druid vs Rockset Architecture

Architecture

Apache Druid

Rockset

Deployment model

• SaaS or self managed. • Imply’s Druid cloud offering requires customers to configure, scale, and capacity plan

SaaS - infrastructure, software and cluster ops managed by service provider

Use of storage hierarchy

• Queries are served from data on disk and an in-memory cache • Cloud storage or HDFS for deep storage

• SSDs store shared hot data, accessible from any Virtual Instance cluster • Cloud storage for durability

Isolation of ingest and query

• Ingestion and queries are run on the same node by default • The ingestion and querying processes can be run on separate nodes, however not for real-time data

Yes - separate compute clusters (Virtual Instances) for ingest and query

Separation of compute and storage

No, although Imply’s offering separates compute and storage

Yes

Isolation for multiple applications

Yes - separate compute cluster (Virtual Instance) for each application

Druid’s architecture employs nodes called data servers that are used for both ingestion and queries. High ingestion or query load can cause CPU and memory contention compared with Druid alternatives. Breaking apart the pre-packaged ingestion and query server components involves planning ahead and additional complexity, and is not dynamic.

Rockset is built to be a cloud-only database and does not have a self-managed option. It disaggregates compute from both hot storage and cloud storage, allowing multiple isolated compute clusters to run on the same shared data.

Apache Druid vs Rockset Ingestion

Ingestion

Apache Druid

Rockset

Data sources

Data connectors to: • Events streams (e.g. Kafka, Kinesis) • Data lakes (e.g. S3, Google Cloud Storage) • RDBMS and HDFS databases CDC events from databases require manual conversion to Druid events

Managed data connectors to: • Events streams (e.g. Kafka, Kinesis) • Database CDC (e.g. MongoDB, DynamoDB, MySQL, PostgreSQL) • Data lakes (e.g. S3, Google Cloud Storage)

Semi structured data

Druid requires flattening nested data at ingest and maintaining a flattening spec as the schema changes over time

• Ingests JSON and XML without a predefined schema • Ingests nested data

Transformations and rollups

Yes, using ingestion specs written in JSON support rollups and simple transformations. SQL ingest transformations available for Imply

Yes - using SQL ingest transformations

Druid has built-in connectors that manage ingestion from common data sources. Unlike some Druid competitors, it doesn’t support nested data, so data must be flattened at ingest. Denormalization is also required at ingest, increasing operational burden for certain use cases.

Rockset has built-in connectors that manage streaming ingestion from common data sources. It has native support for semi-structured data, so that nested JSON and XML can be ingested and queried as is.

Apache Druid vs Rockset Performance

Performance

Apache Druid

Rockset

Updates

Druid segments become immutable once committed and published, making it more appropriate for append-only use cases

Documents are mutable and can be updated at the field level

Indexing

• Bitmap index

• Converged Index (row, columnar and inverted index) built on all data by default • All queries are resolved through the index

Query latency

Typically sub-second query latency for denormalized, flattened datasets up to 100s of TBs

50-1000ms queries on 100s of TB

Storage format

Columnar format partitioned by time

Converged Index, comprising a rowstore, columnstore and inverted index

Streaming ingest

• Sub 15 seconds

• Ingests on a per-record basis • Data latency is typically 1-2 seconds

Druid is designed to make streaming data queryable as quickly as possible. JOINs are either impossible or incur a large performance penalty. Updates are only possible via batch jobs. Druid leverages data denormalization and write-time aggregation at ingestion to reduce query latency.

Rockset is designed to make streaming data queryable as quickly as possible by avoiding the need to batch data. It also updates documents efficiently by only reindexing fields that are part of an update request. Rockset indexes all data by default, which results in storage amplification but also enables low-latency queries that require less compute.

Apache Druid vs Rockset Queries

Queries

Apache Druid

Rockset

Joins

Yes, only for datasets that fit in memory, and with a query latency penalty

Yes

Query language

Druid native queries • Druid SQL

SQL - with extensions for nested data

Developer tooling

• Druid SQL API

• Data APIs - saved SQL queries executed via REST endpoint • Python, Java, Node.js and Go SDKs • UDFs

Visualization tools

Pivot, maintained by Imply

Integrations with Tableau, Looker, Grafana, Superset, Power BI, Retoolwhic

Druid has a native JSON-based query language and provides Druid SQL as an alternative that translates into its native queries. JOINs are not recommended.

Rockset supports SQL as its native query language and can perform SQL joins. Users can create data APIs by storing SQL queries in Rockset that are executed from dedicated REST endpoints. Rockset integrates with some common visualization tools, but BI is not Rockset’s primary use case.

Apache Druid vs Rockset Scalability

Scalability

Apache Druid

Rockset

Vertical scaling

Users can manually increase or decrease server sizes

Resize compute clusters (Virtual Instances) via API or in the console

Horizontal scaling

• Users can manually add additional nodes to a scaled-out cluster. • Imply automates some of the horizontal scaling process.

• Add or remove compute clusters (Virtual Instances) via API or in the console • Scale out compute clusters for higher concurrency • Use separate compute clusters to isolate ingest from query or for multiple isolated applications • No rebalancing required

Druid users are exposed to complex decisions about the number and size of servers as clusters are scaled.

Rockset Virtual Instances are distributed compute clusters that can be scaled up for faster queries or scaled out for practically unlimited concurrency or if compute isolation is needed. Rockset has shared storage that scales automatically and independently, so no rebalancing is required.