Why users are migrating from Apache Druid to StarRocks
Initially launched in 2011, Apache Druid® was once the leader in real-time analytics. Unfortunately, as analytics use cases have expanded and become more demanding and sophisticated, Druid now struggles to meet the performance needs of modern data users. Some of these limitations include:
NOT ANSI SQL COMPATIBLE
Druid provides Druid SQL, a 'SQL Like' query interface. It does not support standard ANSI SQL. Any consuming applications are limited by the functionalities and syntax of Druid SQL.
NO JOINED TABLE SUPPORT
Druid may have great performance for queries running against a single table, but it struggles with querying joined tables.
NO REAL-TIME UPDATES
In Druid, once data is written into a segment, it is impossible to update or delete it (imutable). This limited the usage of Druid in many use cases.
DATED ARCHITECTURE
Based on the Scatter-Gather architecture, Druid is naturally challenged by operations like high cardinality aggregations and precise count distinct.
Why more Druid users are switching to StarRocks
The benefits of StarRocks reach beyond SQL and MySQL compatibility. Here are some additional great reasons to switch:
Free yourself from denormalized tables
Join relationships are the foundation of modern analytics, but they also pose a challenge to query performance.
Apache Druid has tried to circumvent this challenge by focusing on single-table query performance. Because of this, users have to flatten joined tables into a single table in Apache Druid. This step adds pipeline delay and requires extra resources.
StarRocks delivers excellent performance on both single-table queries and joined queries. With StarRocks, users can simplify their data ingestion pipeline, improve data freshness, and cut down on ETL costs.
Embrace mutable data
Mutable data is a common byproduct of business activities. It can be caused by glitches in the underlying data pipeline or it can simply be a part of normal business logic.
Apache Druid, like most other analytical databases, doesn't support UPDATE and DELETE operations natively. Instead, it provides a MUTATION operation to asynchronously ALTER TABLE.
With StarRocks, mutable data is handled natively, and updated analytics results are calculated immediately.
Scale analytics with ease
StarRocks has a Massively Parallel Processing (MPP) architecture. With this architecture, a query request is split into different logical execution units and runs simultaneously on multiple nodes. Each node has its own exclusive resources (CPU, memory) that the MPP architecture can make efficient use of, which enables better horizontal scalability.
In contrast, Druid is built on Scatter-Gather architecture. In this architecture, the Gather component inevitably becomes the bottleneck. That's why Druid struggles with some analytics operations such as high cardinality aggregations and precise count distinct.
StarRocks has also built its native vectorized query engine. The native vectorized engine makes full use of SIMD instructions in CPU to process multiple dataset for every instruction. StarRocks' native vectorized engine improves the overall performance of operators by 3 to 10 times.
Simplify Operations
StarRocks architecture contains a group of Frontend (FE) nodes and Backend (BE) nodes. With no dependencies on any external components, StarRocks is easy to deploy and maintain. Meanwhile, the entire system eliminates single points of failure through replication of meta-data and data.
FE and BE nodes can automatically scale out to support larger data volume or stricter query performance requirements. Data redistribution is handled automatically behind the scene without impacting end users' query experiences.
Druid users would appreciate StarRocks' streamlined architecture since they don't have to manage legacy Hadoop style components such as HDFS, ZooKeeper, etc.
Compare Apache Druid to StarRocks
Designed for the analytics needs of modern enterprises, StarRocks delivers the capabilities and performance. Apache Druid can't say the same.
|
Comparison
|
|
|
|
Architecture |
 Legacy scatter-gather architecture |
Modern MPP architecture |
|
SQL syntax support |
Only partial SQL syntax support |
|
|
High-cardinality aggregation performance |
|
|
|
3rd party dependencies |
|
|
|
Real-time updates |
 |
|
|
Distributed joins |
|
|
|
Data lake query support |
|
|
|
Support for federated queries |
|
|
Apache Druid
StarRocks
Featured success stories
These leading enterprises have made the move from Apache Druid to StarRocks. Learn how they've supercharged query performance, eliminated maintenance work, and accelerated insights:
-1.png)
Talk to an engineer
Have questions about CelerData and StarRocks? You can connect with our team of solutions architects and experienced engineers who can answer all of your questions and even offer a personalized demo aligned with your specific needs and analytics scenarios.