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Elasticsearch vs StarRocks

Compare and contrast Elasticsearch and StarRocks by architecture, ingestion, queries, performance, and scalability.

Compare Elasticsearch to Rockset here

Compare StarRocks to Rockset here

Elasticsearch vs StarRocks Architecture

Deployment model
On-prem, PaaS options
PaaS or self managed
Use of storage hierarchy
• Hot, warm and cold storage on disk • Frozen storage on cloud storage
Data is stored on disk and in memory
Isolation of ingest and query
No - There are dedicated ingestion nodes but indexing, compaction and updates occur on the data nodes
No, but you can limit resources for ingestion and querying separately
Separation of compute and storage
No, but StarRocks supports nodes that don't store data locally
Isolation for multiple applications
Full isolation with replication

Elasticsearch is an open-source distributed search engine built on Apache Lucene, a full text search library. Elasticsearch is a distributed system, which means that it is designed to operate across multiple nodes, each responsible for a part of the data.

StarRocks is a high-performance OLAP database that can be deployed on the cloud or self managed. StarRocks does not separate compute and storage and offers limited options for resource isolation. It offers a robust set of features and high performance but requires considerable expertise to operate and scale.

Elasticsearch vs StarRocks Ingestion

Data sources
• Logstash JDBC input plugin for relational databases • Open-source Kafka plugin or Kafka Elasticsearch Service Sink Connector (available only to managed Confluent and Elasticsearch) • REST APIs or client libraries to sync data directly from the application
Streaming • Kafka • Flink Data lakes • HDFS compatible • Cloud storage
Semi structured data
Yes- Ingests JSON and XML without a predefined schema
Supports columns with JSON data • Does not support mixed-type columns • Support for star and snowflake schemas
Transformations and rollups
Yes - Ingest pipelines can be configured to remove fields, extract values from text and enrich data. Ingest pipelines require ingest nodes in the cluster. Rolling up historical data is in technical preview
Yes, via materialized views

Elasticsearch has a number of integrations as well as a REST API. It is a NoSQL database and natively supports semi-structured data. Transformations typically occur upstream so that data can be modeled for optimal performance before it is indexed in Elasticsearch.

StarRocks ingests data from a variety of sources, including both batch and streaming data. StarRocks can ingest nested JSON data, but enforces type at the column level.

Elasticsearch vs StarRocks Performance

Update API can update, delete or skip modifying the document. The entire document must be reindexed; in-place updates are not supported
While StarRocks is mutable, the update rate is slow, which is why it is most often used for append-only workloads
Inverted index
Columnar index, limited support for inverted indexes
Query latency
50-1000ms queries on 100s of TBs
50-1000ms queries on 100s of TB
Storage format
JSON documents
• StarRocks is a columnstore that organizes data into prefix indexes, per-column data blocks, and per-column indexes • All data is replicated 3 times to achieve both fault-tolerance and concurrency
Streaming ingest
• Ingests on a per-record or batch basis • Data latency on a per-record basis is typically 1-2 seconds
Data latency is typically 1-2 seconds

Elasticsearch is a search engine that utilizes an inverted index. Although this approach leads to storage amplification, it also enables low-latency queries that demand less computation. Elasticsearch is tailored to accommodate large scale, append-only data such as logs, events, and metrics. To manage frequently updated data, users often utilize the Bulk API to minimize computational costs and ensure consistent query performance.

StarRocks was purpose-built for high-performance ingest, low-latency queries, and high concurrency. Optimized performance requires significant manual tuning.

Elasticsearch vs StarRocks Queries

No- Need to use workarounds including data denormalization, application-side joins, nested objects or parent-child relationships
Multi-table join support
Query language
DSL - domain specific language
Developer tooling
• REST API • Java, Javascript, Go, .NET, PHP, Perl, Python, Ruby, Rust
Visualization tools
• Kibana • PowerBI, Qlik, Tableau
Compatibility with MySQL protocols enables StarRocks to work with BI tools

Elasticsearch has its own domain specific language (DSL) based on JSON. Joins are not a first class citizen in Elasticsearch requiring a number of complex and expensive workarounds. Elasticsearch is known for its developer tooling and supports a number of client libraries. Kibana is the visualization layer for Elasticsearch and is frequently used for log analytics and monitoring.

StarRocks uses a high-performance vectorized SQL engine, a custom-built cost-based optimizer, and has support for materialized views.

Elasticsearch vs StarRocks Scalability

Vertical scaling
Manually resize machines
• Both frontend and backend nodes can be manually resized
Horizontal scaling
• Elasticsearch is horizontally scalable and can scale by adding nodes to the cluster • When using managed Elastic, autoscaling policies can be used to self-monitor cluster health and it is the responsibility of the operator to update resource allocations either manually or using APIs. Elasticsearch rebalances the data automatically obeying shard allocation rules • There are many cluster-level operations that need to be monitored when scaling
• Both frontend and backend nodes can be manually scaled horizontally

Elasticsearch is horizontally scalable and can scale by adding more nodes to the cluster. Its tightly coupled architecture means that compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling Elasticsearch often requires deep expertise as there are many levels of the system that need to be managed- the server, operating system, network and software.

StarRocks can scale up or out, but its tightly coupled compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling StarRocks often requires deep expertise as there are many levels of the system that need to be managed.