Rockset vs. Apache Druid
for Real-Time Analytics

Apache Druid was originally developed to deliver real-time analytics capabilities for the ad tech industry. It’s inherently complex, requires both expertise and operational effort to maintain and demands manual tuning to achieve performance. Rockset, in contrast, is cloud native, fully managed, and delivers sub-second SQL out of the box.

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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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.

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If you want flexible and easy real-time analytics, check out Rockset.

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 and compute in the cloud