Clickhouse vs Elasticsearch: A Comprehensive Comparison

ClickHouse and Elasticsearch are two popular data storage solutions, but they have different strengths and weaknesses.

ClickHouse is a column-store database management system, designed to handle large amounts of data and provide high-performance analytics.

Elasticsearch, on the other hand, is a NoSQL search engine, optimized for fast search and aggregation of data.

While both solutions are powerful tools for data analysis, they cater to different needs and use cases.

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ClickHouse is an open-source columnar database management system for online analytical processing (OLAP) tasks, optimized for large-scale data operations.

It handles high-performance querying and is popular in industries that require real-time data insights. ClickHouse supports SQL queries, making it accessible to those familiar with traditional relational databases.

Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene, known for scalability, speed, and reliability. It's a top choice for full-text search and log analytics, and can index structured, unstructured, time-series, and geospatial data.

Elasticsearch is highly extensible and integrates with various data sources and visualization tools, allowing users to query and visualize results quickly. Its strength lies in its ability to perform complex searches and data analysis in near real-time.

Take a look at this: Lakehouse vs Data Warehouse

ClickHouse is an open-source columnar database management system for online analytical processing (OLAP) tasks. It handles high-performance querying and is optimized for large-scale data operations.

ClickHouse was originally built by Yandex for Yandex.Metrica, a massive analytics tool popular in Russia, and eventually spun out into an independent, open-source project.

Its architecture allows for efficient data compression, reducing storage costs while enabling rapid data retrieval. ClickHouse is popular in industries that require real-time data insights.

ClickHouse can be deployed through various managed, closed-source solutions, including ClickHouse Cloud and Altinity Cloud. ClickHouse Cloud includes a GUI for querying and visualizing data.

Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It's known for scalability, speed, and reliability, making it a top choice for full-text search and log analytics.

Elasticsearch can index structured, unstructured, time-series, and geospatial data, allowing users to query and visualize results quickly. It supports a variety of data types and provides built-in functions for data aggregation and filtering.

ClickHouse is designed to return aggregate values of big data at millisecond speeds, using a series of clever techniques, including a columnar store and dynamic materialized views.

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Elasticsearch is best understood by separating the virtual structures from the physical structures. This means looking at the logical organization of data versus the actual hardware and software components that make it up.

Elasticsearch's structure is often described as a layer cake, with multiple layers built on top of each other. This helps to clarify the different components that make up the system.

By separating the virtual structures from the physical structures, we can better understand the different components of Elasticsearch, such as the index, type, and document. Each of these components plays a crucial role in the overall functioning of the system.

This approach also helps to highlight the importance of indexing in Elasticsearch, which can be thought of as a virtual structure that organizes data in a way that makes it easily searchable.

Index mappings play a crucial role in how data is stored and accessed in Elasticsearch. By utilizing only the keyword and date data types, we can switch off all segment data structures except inverted index, doc_values, and Bkd trees for efficient data analytics.

Explore further: Remove Elasticsearch Index

For a fair comparison with ClickHouse, we chose to store the fields country_code, project, url, and timestamp in the index. This allows for fast filtering, aggregations, and sorting.

The keyword type populates the inverted index and doc_values, making it suitable for aggregations and sorting. It also implies no normalization and tokenization of field values, which are inserted unmodified into the inverted index for exact match filtering.

We could have further optimized Elasticsearch data storage by disabling the inverted index for fields like project and url, but we kept it for these fields as they are also part of the ClickHouse table's primary key.

The date type is internally stored as a long number in both doc_values and Bkd trees, supporting aggregations, sorting, and range queries. We didn't switch off Bkd trees for the date field in Elasticsearch.

There are two types of index mappings: one that stores _source and one that doesn't. Disabling _source reduces data storage size, but makes reindex and update operations impossible.

Here's a comparison of the two index mapping types:

ClickHouse and Elasticsearch are both powerful tools for managing and analyzing large amounts of data. One key feature that sets them apart is their approach to data storage and processing.

ClickHouse allows you to store data on multiple servers, with each server being a group of replicas for fault tolerance. This means that if one server fails, the data is still available on the other replicas.

Elasticsearch, on the other hand, is designed for scalability and can add nodes to a cluster as needed to increase capacity and reliability. This makes it well-suited for handling large amounts of data and high traffic.

ClickHouse uses a vector computation engine to process data, which enables high CPU efficiency and fast query performance. This is particularly useful for handling complex queries and large datasets.

Elasticsearch supports a wide range of data types, including structured, unstructured, and text data. This makes it a versatile tool for working with different types of data.

Here's a comparison of the key features of ClickHouse and Elasticsearch:

ClickHouse also supports real-time data inserts, which allows you to continually add data to a table without any locks. This is particularly useful for applications that require high data freshness and availability.

Elasticsearch's rollup feature allows you to summarize and store historical data, which can help reduce storage costs and improve query performance. This is a useful feature for applications that require long-term data storage and analysis.

Explore further: Elasticsearch Tiered Storage

Configuring ClickHouse is much simpler compared to Elasticsearch and requires less upfront planning and setup code. This makes it a great choice for those who want to get started quickly.

ClickHouse uses a shared-nothing architecture, which allows it to form data clusters from multiple nodes. Each node has its own storage and compute resources.

The simplicity of ClickHouse setup is a major advantage over Elasticsearch, which requires more planning and setup code. This can be a significant time-saver for those who want to focus on analyzing data rather than setting it up.

Loading data into your system is a crucial step, and it's good to know that ClickHouse supports loading data from Parquet files out-of-the-box.

Parquet is the standard for distributing analytics data in 2024, making it a popular choice for many organizations.

Loading data from Parquet files hosted in a GCS bucket is a common use case, but it's worth noting that Elasticsearch doesn't have native support for this file format.

Logstash, Elasticsearch's recommended ETL tool, also lacks support for Parquet files at the time of writing, which can make data loading more complicated than it needs to be.

Setting up ClickHouse is a breeze compared to other solutions like Elasticsearch.

Configuring ClickHouse requires less upfront planning and setup code.

In fact, it's much simpler, which is a welcome relief for many developers.

This streamlined setup process makes it easier to get started and focus on more important tasks.

ClickHouse's setup is a great example of how simplicity can be a major advantage.

In ClickHouse, data clusters are formed from multiple nodes using a shared-nothing architecture, where each node has its own storage and compute resources.

Each node in a ClickHouse cluster processes data queries independently and in parallel, thanks to its design.

ClickHouse's columnar layout, dynamic materialized views, and specialized engines enable it to return aggregations like sums, averages, and standard deviations.

Elasticsearch, on the other hand, uses a distributed document-oriented architecture, allowing you to store data in JSON format and use its inverted index based on Apache Lucene for searchability.

In Elasticsearch, data is stored as documents, grouped into shards, which are part of physical and virtual collections known as nodes and indices.

ClickHouse Cloud services feature three compute nodes by default, with incoming queries routed via a load balancer to one specific node that runs the query.

You can scale the size or number of compute nodes in a ClickHouse Cloud service manually or automatically, allowing for flexible infrastructure management.

Here's a comparison of ClickHouse and Elasticsearch architectures:

ClickHouse employs a columnar storage format that compresses and retrieves data quickly, requiring 9 times less storage than Elasticsearch and over 14 times less than Elasticsearch with ZSTD compression. This is a significant advantage for large-scale data storage.

ClickHouse's columnar storage format allows it to read only the necessary columns for a query, speeding up data access and improving query performance for analytical tasks. This approach is effective for batch processing and analytical queries where full table scans are common and acceptable.

Elasticsearch, on the other hand, uses a distributed document-based approach for data storage, storing data in JSON-like documents. This allows for flexible schema and quick data retrieval, making Elasticsearch suitable for full-text search and real-time analytics applications.

Recommended read: Elasticsearch Match Query

Indexing is a crucial aspect of both Elasticsearch and ClickHouse, allowing for high-speed search capabilities and efficient data retrieval.

Elasticsearch heavily relies on indexing to support its search capabilities, indexing every document to enable rapid search by keywords, terms, or patterns.

A different take: Elasticsearch Fuzzy Query

ClickHouse indexes are based on an alternative to the B-Tree index, Sparse Indexing, which involves considering an index for every granule (group of data) instead of every row.

Elasticsearch uses inverted indexing built on Apache Lucene, making it easier to search for specific terms on documents within large datasets. This is achieved by categorizing data into terms, such as words or phrases, and the corresponding document IDs.

Elasticsearch indexing can be optimized by disabling the inverted index for fields that are not filtered on, such as the project and url fields in the example.

For optimal indexing, Elasticsearch stores the date field as a long number in both doc_values and Bkd trees, supporting aggregations and sorting.

Here's a comparison of the indexing methods used by Elasticsearch and ClickHouse:

Materialized views in ClickHouse are equivalent to Elasticsearch transforms for pre-calculating aggregations, enabling continuous data transformation.

By understanding the indexing methods used by Elasticsearch and ClickHouse, you can optimize your data storage and retrieval for efficient performance.

ClickHouse requires significantly less storage than Elasticsearch, with storage sizes ranging from 36.3 GB to 38.3 GB for equivalent compression levels.

One key reason for this difference is ClickHouse's columnar storage format, which compresses and retrieves data quickly. This format allows ClickHouse to read only the necessary columns for a query, speeding up data access and improving query performance.

Elasticsearch, on the other hand, uses a distributed document-based approach for data storage, storing data in JSON-like documents. This approach allows for flexible schema and quick data retrieval, making Elasticsearch suitable for full-text search and real-time analytics applications.

ClickHouse's columnar storage system also means it doesn't use traditional indexing methods, reducing storage overhead and maintenance complexity. This approach is effective for batch processing and analytical queries where full table scans are common and acceptable.

Compressed data in ClickHouse is stored with an inverted structure, similar to traditional PostgreSQL or MySQL tables, using methods such as Z Standard (ZSTD) and LZ4.

On a similar theme: Elasticsearch Search Dsl

ClickHouse and Elasticsearch are both designed for scalability, but they approach it in different ways. ClickHouse is capable of efficiently handling petabytes of data across distributed clusters, while Elasticsearch provides horizontal scaling by distributing data across multiple nodes.

Elasticsearch's architecture allows for dynamic scaling, accommodating increased data loads and search queries without a drop in performance. This scalability makes it suitable for enterprises that require robust search capabilities and real-time analytics.

ClickHouse, on the other hand, uses materialized views and specialized engines optimized for mathematical queries traversing numeric data to enable millisecond-level fetches for queries. This pre-calculation of aggregations in advance allows for efficient handling of extensive datasets.

Here's a comparison of the two platforms' scalability features:

In terms of performance, ClickHouse is designed to handle large-scale data analysis and requires reliable performance as data volumes grow. Its ability to partition work across nodes facilitates continuous operations and efficient resource utilization.

To optimize your data pipelines and architectures, it's essential to consider the differences between ClickHouse and Elasticsearch. Leveraging Materialized Views in ClickHouse can significantly speed up query performance for repetitive analytical tasks.

ClickHouse's Merge Tree engine offers advanced tuning parameters like max_partitions_to_read and max_threads, which can be adjusted to balance performance and resource usage. Fine-tuning these settings can drastically improve query performance on high-concurrency workloads.

Elasticsearch can be optimized with Index Templates and Lifecycle Management (ILM) policies to automate the creation, optimization, and deletion of indices. This helps manage storage costs and maintain optimal query performance as data grows.

To prevent resource exhaustion in Elasticsearch, configure circuit breakers to manage memory usage during large queries. You can also use query caching for frequently run queries to boost performance and reduce processing times.

Here are some key takeaways to keep in mind:

Scalability is a crucial aspect of any database solution, and both ClickHouse and Elasticsearch excel in this area. ClickHouse is designed for scalability, capable of handling petabytes of data across distributed clusters, while Elasticsearch provides horizontal scaling by distributing data across multiple nodes.

This scalability is beneficial for industries dealing with large-scale data analysis, ensuring reliable performance as data volumes grow. ClickHouse's architecture supports horizontal scaling, allowing organizations to add nodes and distribute workloads without sacrificing performance.

Elasticsearch is equally renowned for its scalability, providing dynamic scaling to accommodate increased data loads and search queries without a drop in performance. Its architecture ensures data redundancy and fault tolerance, crucial for maintaining system uptime.

Here's a comparison of the scalability features of ClickHouse and Elasticsearch:

Both solutions allow for the addition of nodes to the cluster for better scalability, but ClickHouse's distributed system architecture and columnar storage make it a highly scalable solution for extensive datasets.

Peak memory usage is a crucial aspect of performance and scalability. ClickHouse tracks and reports queries' peak memory consumption through the query_log system table.

We use the memory_usage field to track this information. As a bonus, we also report the data processing throughput for some queries. This can be calculated from query_log fields like read_rows and read_bytes divided by query_duration_ms.

Elasticsearch, on the other hand, doesn't directly track queries' peak memory consumptions within the JVM memory. The search profiling API only profiles queries' CPU usage.

The Cluster stats API returns cluster and node-level metrics and statistics like current peak JVM memory usage. However, these statistics consider all queries and wider processes, including background segment merges which can be memory intensive.

It's tricky to correlate these statistics with a specific query run. Therefore, our benchmark results don't report peak memory usage for the Elasticsearch queries.

Here's an interesting read: Bulk Search Elasticsearch

ClickHouse runs aggregation queries over raw data sets at least 5 times faster than Elasticsearch, requiring 4 times cheaper hardware for comparable latencies.

For large data sets, ClickHouse requires 12 to 19 times less storage space, allowing smaller and cheaper hardware to be used.

ClickHouse features a much more storage- and compute-efficient continuous data summarization technique, further lowering computing and storage costs.

Elasticsearch ESQL query has the highest runtime of 9.2 seconds for benchmark query ②, while the equivalent query DSL variant runs significantly faster (256 ms).

Related reading: Query Elasticsearch

ClickHouse runs benchmark query ② over 5 times faster than Elasticsearch when using pre-aggregated counts, using ~ 14 MB of RAM.

Here are some key differences in aggregations and caching between ClickHouse and Elasticsearch:

ClickHouse's sophisticated hash table framework allows it to choose the fastest hash table for each aggregation query, resulting in improved performance.

ClickHouse's vectorized query engine with parallelized execution across all server and cluster resources enables it to utilize hardware up to the theoretical limits.

ClickHouse's modern SQL dialect and rich data types make it well-suited for data analytics, with over 90 pre-built aggregation functions and support for incremental aggregation of large data sets.

Worth a look: Elasticsearch Aggregation

ClickHouse and Elasticsearch are two powerful tools with distinct strengths and weaknesses. ClickHouse excels in high-performance analytical queries, while Elasticsearch shines in full-text search and real-time analytics.

ClickHouse is ideal for structured, time-series data, whereas Elasticsearch thrives with unstructured, searchable content. This means if you're working with data that has a clear structure, such as customer information or sales records, ClickHouse is a great choice.

Elasticsearch, on the other hand, is optimized for swift search results across large document stores. This makes it perfect for applications like text search, monitoring, and log analysis.

Here are the key differences between ClickHouse and Elasticsearch:

Ultimately, the choice between ClickHouse and Elasticsearch depends on your specific use case and requirements.

ClickHouse is well-suited for use cases requiring efficient analytical data processing, such as time-series data analysis and real-time analytics. It's perfect for industries that process enormous data volumes, like finance and telecommunications.

Elasticsearch, on the other hand, is ideal for scenarios requiring full-text searches and log analysis, such as operational monitoring and application performance management. It's a great fit for eCommerce search engines and social media analytics.

Both ClickHouse and Elasticsearch offer real-time data processing capabilities, making them suitable for high-performance data and analytics tasks, including data warehousing and clickstream analytics. ClickHouse also supports common SQL queries, allowing for effortless integration with typical data processing workflows.

ClickHouse is perfect for handling enormous data volumes, making it a great fit for industries like finance and telecommunications. It can process complex queries on vast datasets in real-time, making it ideal for data warehousing and clickstream analytics.

Elasticsearch, on the other hand, is excellent for full-text searches, log analysis, and real-time data monitoring. Its ability to handle diverse data types and perform fast queries makes it ideal for eCommerce search engines and social media analytics.

ClickHouse's rapid query performance and cost-effective storage solutions enable organizations to gain insights from complex and large datasets. This is especially useful for industries that process massive amounts of data.

Elasticsearch's versatile and real-time search capabilities make it perfect for building search engines, monitoring application performance, and analyzing data logs. It's also great for identifying valuable trends, anomalies, and rectifying issues for large data volume searches.

Organizations can use ClickHouse to efficiently manage billions of rows and perform quick aggregations. Its high-performance data and analytics tasks make it a great choice for handling complex queries.

ClickHouse offers different connectors and APIs for easy data integration, making it simple to ingest data from various sources.

Elasticsearch provides a robust ecosystem with a wide scope of integration, including plugins for machine learning, visualization, monitoring, and security.

Airbyte is a no-code data movement platform that simplifies the process of building a data pipeline, allowing you to move data from varied sources into the destination of your choice.

Airbyte supports 400+ pre-built connectors, making it easy to integrate with both ClickHouse and Elasticsearch.

Here are some key features of Airbyte:

Airbyte's UI, API, Terraform Provider, and PyAirbyte make it easy to build pipelines, and its connectors enable a ClickHouse-Elasticsearch connection.