Elasticsearch vs ClickHouse

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

Compare Elasticsearch to Rockset here

Compare ClickHouse to Rockset here

Elasticsearch vs ClickHouse Architecture

Architecture

Elasticsearch

ClickHouse

Deployment model

On-prem, PaaS options

• Self-managed on-premises or on cloud infrastructure • Several managed cloud services available

Use of storage hierarchy

• Hot, warm and cold storage on disk • Frozen storage on cloud storage

• Designed to use hard disk drives for storage • Can also use SSD if available

Isolation of ingest and query

No - There are dedicated ingestion nodes but indexing, compaction and updates occur on the data nodes

Separation of compute and storage

No - although ClickHouse Cloud decouples compute and cloud storage

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.

ClickHouse is open source and can be deployed anywhere. Several vendors such as ClickHouse, the company, and Tinybird also offer cloud versions. Compute and storage are tightly coupled, although ClickHouse Cloud was rearchitected to decouple compute and storage. ClickHouse Cloud pricing is based on compute and storage usage.

Elasticsearch vs ClickHouse Ingestion

Ingestion

Elasticsearch

ClickHouse

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

• Core integrations for ingestion from Kafka, S3, Google Cloud Storage • Other partner and community integrations available

Semi structured data

Yes- Ingests JSON and XML without a predefined schema

• JSON Object type for handling nested JSON • Automatically infers schema from a subset of rows

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 - several storage engines can pre-aggregate data

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.

ClickHouse has core integrations from common sources such as Kafka and S3. It recently introduced greater ability to handle semi-structured data using the JSON Object type and automatic schema inference.

Elasticsearch vs ClickHouse Performance

Performance

Elasticsearch

ClickHouse

Updates

Update API can update, delete or skip modifying the document. The entire document must be reindexed; in-place updates are not supported

• Writes to immutable files • Updates rewrite and merge data files asynchronously • Frequent updates are not recommended due to potential for large rewrites

Indexing

Inverted index

• Primary indexes use sparse indexing on data ordered by primary key • Secondary data skipping indexes

Query latency

50-1000ms queries on 100s of TBs

Sub-100ms to seconds, optimized for large-scale aggregations

Storage format

JSON documents

• Column-oriented • Heavily compressed to minimize storage footprint

Streaming ingest

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

Recommends inserting in batches of >1000 rows and <1 insert per second

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.

ClickHouse leverages column orientation and heavy compression for better performance on analytics workloads. It also uses indexing to accelerate queries as well. While ClickHouse use cases often involve streaming data from Kafka, batching data is recommended for efficient ingestion.

Elasticsearch vs ClickHouse Queries

Queries

Elasticsearch

ClickHouse

Joins

No- Need to use workarounds including data denormalization, application-side joins, nested objects or parent-child relationships

Yes

Query language

DSL - domain specific language

SQL

Developer tooling

• REST API • Java, Javascript, Go, .NET, PHP, Perl, Python, Ruby, Rust

• API for querying SQL via POST command • Python, Java, Node.js and Go language clients

Visualization tools

• Kibana • PowerBI, Qlik, Tableau

Integrations with Metabase, Superset, Grafana, Tableau, Deepnote and Rocket BI

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.

ClickHouse uses SQL for querying, with support for SQL joins. ClickHouse integrates with some common tools for visual analytics, including Superset, Grafana and Tableau.

Elasticsearch vs ClickHouse Scalability

Scalability

Elasticsearch

ClickHouse

Vertical scaling

Manually resize machines

Scale up single-node ClickHouse for vertical scaling

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

• Compute and storage scaled in lockstep • Data rebalanced to populate newly added nodes • Cloud offerings automate some of the scaling and rebalancing effort

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.

ClickHouse can be used in both single-node and distributed modes. Tight coupling of compute and storage and the need to rebalance data make scaling out more complex, but cloud versions of ClickHouse help automate this process.