Rockset vs. Apache Druid
for Real-Time Analytics

Apache Druid is a real-time analytics database built for the datacenter era. It cannot exploit the efficiency and simplicity of the cloud, making it challenging to achieve performance at scale. Rockset is built for the cloud with compute-storage and compute-compute separation so you no longer need to overprovision resources. Save infrastructure costs and operational effort with a cloud native, fully-managed solution.

20%

Less Compute per Query

Rockset separates storage, ingest and query compute so you don’t need to overprovision resources for your workload. Furthermore, Rockset can save up to 100x on storage costs by using SQL-based rollups.

1.12x

Faster Query Performance

Rockset is 1.12 times faster than Druid with the same hardware configuration based on results from the Star Schema Benchmark (SSB).

20x

Faster Development Time

Rockset is cloud-native, saving your team from needing to manage clusters, nodes, shards and indexes. Furthermore, Rockset’s Converged Index enables ad-hoc analytics without performance tuning so teams realize real-time analytics 20x faster.

Whatnot logo
Rockset offers ultimate flexibility for us to quickly experiment and build features.

Xin Xia, Marketplace and Discovery

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Command Alkon logo
We absolutely love Rockset. It’s a game changer for us.

Doug Moore, VP of Cloud

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Compute and Storage
Druid’s tightly coupled architecture leads to inefficient resource utilization
Druid has a tightly coupled architecture with storage and compute being collocated for fast performance. Ingestion and queries run on the same cluster, competing for resources. There is no isolation for multiple applications, requiring resources to be overprovisioned.
Operational Burden
Druid is complex, distributed, and user-managed
Druid users are responsible for configuring, scaling, and capacity planning, even with the PaaS offering. The lack of independent scaling of storage and compute makes ongoing administration and dealing with evolving workload demands an operational challenge.
Performance Engineering
Druid requires constant tuning to achieve high performance
Druid requires time-consuming manual configuration and tuning to get good query performance whenever new data or queries are introduced. Untuned queries will not perform well.
JOINs
JOINs in Druid are slow, difficult, and limited
Druid recommends avoiding JOINS and opting for denormalization. Because Druid only supports broadcast JOINs, one table must fit into memory on a single server, making large table JOINs impossible. Implementing broadcast JOINs results in a 300% query latency penalty.
Nested Data
Druid does not natively support nested data
Druid requires flattening nested data at ingest and maintaining a flattening spec as the schema changes over time. Handling constantly-changing nested data is burdensome.

Resources



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Rockset Beats ClickHouse and Druid on the Star Schema Benchmark (SSB)

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Introducing Compute-Compute Separation for Real-Time Analytics

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How to Handle Database Joins in Apache Druid vs Rockset

This article focuses on implementing database joins in Apache Druid, explores workarounds like denormalization and examines alternative solutions like Rockset.

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How to Handle Nested Data in Apache Druid vs Rockset

Nested data needs to be flattened upon ingestion when using Apache Druid. We look at how to ingest and query nested data in Druid vs alternatives like Rockset.

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Rockset is built to exploit the efficiency of the cloud for real-time analytics, delivering consistent performance at a fraction of the cost.

Here are four reasons why:

Converged Indexing™

Creation of search, columnar and row indexes at ingest time

Full SQL

SQL search, aggregations and joins on semi-structured data

Mutability

Efficient inserts, updates and deletes

Cloud-Native Architecture

Independent scaling of storage-compute and compute-compute