[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"stack-clickhouse-en":3},{"data":4,"meta":348},[5],{"id":6,"documentId":7,"title":8,"slug":9,"excerpt":10,"difficulty":11,"estimatedCost":12,"maturity":13,"seoTitle":14,"seoDescription":15,"createdAt":16,"updatedAt":16,"publishedAt":17,"coverImage":18,"category":68,"tags":77,"author":105,"sections":113,"officialLinks":321,"relatedStacks":337,"faq":338},10,"y3nyqc1a2am861xi3gk3m47o","ClickHouse","clickhouse","A high-performance, open-source column-oriented database management system designed for real-time online analytical processing (OLAP).","intermediate","Free (Open Source) / Paid (Cloud/Enterprise hosting)","stable","ClickHouse: High-Performance Columnar OLAP Database","Discover ClickHouse, the open-source columnar database designed for real-time analytical queries, high-volume data ingestion, and distributed OLAP.","2026-07-13T10:16:20.920Z","2026-07-13T10:16:20.945Z",{"id":19,"documentId":20,"name":21,"alternativeText":22,"caption":23,"focalPoint":24,"width":25,"height":26,"formats":27,"hash":62,"ext":31,"mime":32,"size":63,"url":64,"previewUrl":24,"provider":65,"provider_metadata":24,"createdAt":66,"updatedAt":66,"publishedAt":67},8,"k29b8org6x7qv7in1t5315o4","clickhouse-cover","An abstract technical illustration showing vertical columns of data flowing into a central processing engine over a dark grid background.","StackAtlas editorial cover",null,1200,630,{"thumbnail":28,"small":38,"medium":46,"large":54},{"name":29,"hash":30,"ext":31,"mime":32,"path":24,"width":33,"height":34,"size":35,"sizeInBytes":36,"url":37},"thumbnail_clickhouse-cover","thumbnail_clickhouse_cover_71f4195a80",".png","image/png",245,129,34.57,34573,"/uploads/thumbnail_clickhouse_cover_71f4195a80.png",{"name":39,"hash":40,"ext":31,"mime":32,"path":24,"width":41,"height":42,"size":43,"sizeInBytes":44,"url":45},"small_clickhouse-cover","small_clickhouse_cover_71f4195a80",500,263,121.65,121649,"/uploads/small_clickhouse_cover_71f4195a80.png",{"name":47,"hash":48,"ext":31,"mime":32,"path":24,"width":49,"height":50,"size":51,"sizeInBytes":52,"url":53},"medium_clickhouse-cover","medium_clickhouse_cover_71f4195a80",750,394,255.89,255885,"/uploads/medium_clickhouse_cover_71f4195a80.png",{"name":55,"hash":56,"ext":31,"mime":32,"path":24,"width":57,"height":58,"size":59,"sizeInBytes":60,"url":61},"large_clickhouse-cover","large_clickhouse_cover_71f4195a80",1000,525,413.36,413360,"/uploads/large_clickhouse_cover_71f4195a80.png","clickhouse_cover_71f4195a80",109.1,"/uploads/clickhouse_cover_71f4195a80.png","local","2026-07-13T10:16:20.565Z","2026-07-13T10:16:20.566Z",{"id":69,"documentId":70,"name":71,"slug":72,"description":73,"createdAt":74,"updatedAt":75,"publishedAt":76},2,"b8wufvxzgfmvskayspbptuwh","Data Engineering","data-engineering","Data pipelines, processing engines, lakehouses, warehouses, streaming platforms, and analytical infrastructure.","2026-07-11T16:09:26.106Z","2026-07-15T15:43:57.439Z","2026-07-15T15:43:57.433Z",[78,86,93,97],{"id":79,"documentId":80,"name":81,"slug":82,"createdAt":83,"updatedAt":84,"publishedAt":85},5,"tiq4bj6b1r4tk1gp4fwffjdz","OLAP","olap","2026-07-11T16:19:59.426Z","2026-07-15T10:16:21.250Z","2026-07-15T10:16:21.245Z",{"id":19,"documentId":87,"name":88,"slug":89,"createdAt":90,"updatedAt":91,"publishedAt":92},"kulfumikh6rmscrvyux4vife","Distributed Computing","distributed-computing","2026-07-12T10:16:06.012Z","2026-07-21T10:16:16.575Z","2026-07-21T10:16:16.569Z",{"id":6,"documentId":94,"name":8,"slug":9,"createdAt":95,"updatedAt":95,"publishedAt":96},"derlxzzvcqr8kx2wnxkbq7v0","2026-07-13T10:16:15.772Z","2026-07-13T10:16:15.769Z",{"id":98,"documentId":99,"name":100,"slug":101,"createdAt":102,"updatedAt":103,"publishedAt":104},11,"xvgylqykbxll774rmhh9h2ii","Columnar Database","columnar-database","2026-07-13T10:16:15.932Z","2026-07-15T10:16:21.323Z","2026-07-15T10:16:21.318Z",{"id":106,"documentId":107,"name":108,"slug":109,"bio":24,"createdAt":110,"updatedAt":111,"publishedAt":112},1,"lv2wpsnmnajx4jmhrvo1zne6","Jose Henriquez","jose-henriquez","2026-07-04T16:49:01.335Z","2026-07-04T16:49:39.022Z","2026-07-04T16:49:39.004Z",[114,133,197,232,263,294],{"id":115,"type":116,"title":117,"content":118},55,"overview","Overview",[119,125,129],{"type":120,"children":121},"paragraph",[122],{"type":123,"text":124},"text","ClickHouse is an open-source, high-performance column-oriented database management system (DBMS) designed specifically for online analytical processing (OLAP). Originally developed to power the web analytics tools of a major search engine, ClickHouse was open-sourced in 2016 and is now maintained by ClickHouse Inc. It has rapidly become a cornerstone of modern real-time data platforms, prized for its ability to execute complex analytical queries over billions of rows with sub-second latencies.",{"type":120,"children":126},[127],{"type":123,"text":128},"Traditional relational databases, such as PostgreSQL or MySQL, are row-oriented. They store all values belonging to a single row together on disk. This design is highly efficient for online transaction processing (OLTP) workloads, where applications frequently read, write, or update individual records. However, when executing analytical queries—such as calculating the average purchase value across millions of transactions—a row-oriented database must read every single row from disk, including columns that are irrelevant to the query. This introduces massive disk I/O bottlenecks.",{"type":120,"children":130},[131],{"type":123,"text":132},"ClickHouse solves this problem by organizing data column-by-column. In a columnar database, all values for a specific column are stored together on disk. When an analytical query requests only a few columns, ClickHouse reads only those specific columns, drastically reducing disk I/O. Combined with vectorized query execution, aggressive data compression, and parallel processing, ClickHouse maximizes hardware efficiency. It scales horizontally from a single server to distributed clusters containing thousands of nodes, handling petabytes of data while maintaining exceptional query throughput.",{"id":134,"type":135,"title":136,"content":137},56,"architecture","Architecture",[138,142,148,152,156,160,164,168,172,189,193],{"type":120,"children":139},[140],{"type":123,"text":141},"ClickHouse's architecture is engineered from the ground up to eliminate hardware bottlenecks and extract maximum performance from CPUs, memory, and storage systems.",{"type":143,"level":144,"children":145},"heading",3,[146],{"type":123,"text":147},"Columnar Storage and Specialized Compression",{"type":120,"children":149},[150],{"type":123,"text":151},"Data in ClickHouse is stored in columns, with each column written to its own set of files on disk. Because values within a single column share the same data type and often exhibit repetitive patterns, they are highly compressible. ClickHouse applies specialized compression algorithms and codecs to minimize storage footprints. In addition to general-purpose compression algorithms like LZ4 and ZSTD, ClickHouse supports specialized codecs such as DoubleDelta, Gorilla, and T64. These codecs are tailored for specific data types, such as timestamps or floating-point numbers, allowing for compression ratios that often exceed 10x compared to raw data. This reduction in physical size directly translates to faster query speeds, as less data needs to be read from disk into memory.",{"type":143,"level":144,"children":153},[154],{"type":123,"text":155},"Vectorized Query Execution",{"type":120,"children":157},[158],{"type":123,"text":159},"Traditional database engines process queries row-by-row, introducing significant CPU overhead due to virtual function calls, conditional branching, and poor CPU cache utilization. ClickHouse implements vectorized query execution. Instead of processing a single value at a time, operations are performed on vectors—arrays of data points containing thousands of values. This approach allows ClickHouse to leverage SIMD (Single Instruction, Multiple Data) instructions on modern processors. With SIMD, the CPU can perform mathematical or logical operations on multiple data points within a single clock cycle, dramatically accelerating data processing speeds.",{"type":143,"level":144,"children":161},[162],{"type":123,"text":163},"The MergeTree Engine Family",{"type":120,"children":165},[166],{"type":123,"text":167},"The core of ClickHouse's storage layer is the MergeTree engine family. The standard MergeTree engine organizes data by a primary key, sorting the data on disk to enable fast range queries. Unlike traditional B-trees, ClickHouse uses sparse indexes. A sparse index does not point to individual rows; instead, it points to blocks of rows (typically every 8,192 rows). This allows the index to fit entirely in memory, even for tables containing billions of records.",{"type":120,"children":169},[170],{"type":123,"text":171},"Data is written to the database in immutable \"parts.\" In the background, ClickHouse continuously runs merge processes that combine these parts, sorting the data and applying optimizations. Specialized engines within the MergeTree family extend this behavior:",{"type":173,"format":174,"children":175},"list","unordered",[176,181,185],{"type":177,"children":178},"list-item",[179],{"type":123,"text":180},"ReplacingMergeTree: Automatically deduplicates data based on a primary key during background merges, making it useful for handling eventual consistency and updates.",{"type":177,"children":182},[183],{"type":123,"text":184},"SummingMergeTree: Aggregates numerical columns with the same primary key during background merges, reducing storage and accelerating pre-aggregated queries.",{"type":177,"children":186},[187],{"type":123,"text":188},"AggregatingMergeTree: Stores intermediate states of aggregate functions, enabling highly efficient incremental aggregation of complex metrics.",{"type":143,"level":144,"children":190},[191],{"type":123,"text":192},"Distributed Query Processing and Replication",{"type":120,"children":194},[195],{"type":123,"text":196},"ClickHouse achieves horizontal scalability through sharding and replication. Sharding splits data across multiple nodes, allowing queries to run in parallel across the cluster. Replication ensures high availability and fault tolerance by maintaining identical copies of data parts on multiple nodes. Coordination between replicas is managed by ClickHouse Keeper—a lightweight, built-in C++ alternative to Apache ZooKeeper—or ZooKeeper itself. When a distributed query is executed, a coordinator node distributes the work to the relevant shards, gathers the intermediate results, and performs the final aggregation before returning the result to the client.",{"id":198,"type":199,"title":200,"content":201},57,"pros","Strengths",[202,206],{"type":120,"children":203},[204],{"type":123,"text":205},"ClickHouse offers several compelling advantages that make it a leading choice for analytical workloads:",{"type":173,"format":174,"children":207},[208,212,216,220,224,228],{"type":177,"children":209},[210],{"type":123,"text":211},"Blazing-Fast Query Performance: By combining columnar storage, vectorized execution, and parallel processing, ClickHouse delivers query execution speeds that are often orders of magnitude faster than traditional relational databases or general-purpose big data frameworks.",{"type":177,"children":213},[214],{"type":123,"text":215},"Exceptional Data Compression: The columnar format and specialized codecs allow ClickHouse to compress data by 3x to 10x or more compared to uncompressed raw formats. This significantly lowers storage costs and improves performance by reducing disk read operations.",{"type":177,"children":217},[218],{"type":123,"text":219},"Real-Time Data Ingestion: ClickHouse is designed to handle massive write throughput. It can ingest millions of rows per second in real time, making it highly compatible with streaming platforms like Apache Kafka, Redpanda, or AWS Kinesis.",{"type":177,"children":221},[222],{"type":123,"text":223},"Rich SQL Dialect: ClickHouse supports standard SQL alongside powerful analytical extensions. It includes built-in functions for array manipulation, JSON parsing, IP address handling, geospatial analysis, and approximate query processing (e.g., HyperLogLog for unique counts).",{"type":177,"children":225},[226],{"type":123,"text":227},"Resource Efficiency: ClickHouse is written in highly optimized C++ and is engineered to extract maximum performance from available hardware. It utilizes all available CPU cores and memory bandwidth efficiently, meaning organizations can often achieve their performance targets with smaller, less expensive hardware footprints.",{"type":177,"children":229},[230],{"type":123,"text":231},"Flexible Integration Ecosystem: It provides native integrators and connectors for a wide range of data sources, visualization tools, and programming languages, simplifying its integration into existing data pipelines.",{"id":233,"type":234,"title":235,"content":236},58,"cons","Limitations and Trade-offs",[237,241],{"type":120,"children":238},[239],{"type":123,"text":240},"While ClickHouse is exceptionally powerful, it is not a universal solution and comes with specific trade-offs:",{"type":173,"format":174,"children":242},[243,247,251,255,259],{"type":177,"children":244},[245],{"type":123,"text":246},"No Multi-Row ACID Transactions: ClickHouse does not support traditional multi-statement transactions (BEGIN/COMMIT) with ACID guarantees across multiple tables. It is designed for analytical workloads where data is appended in bulk rather than modified incrementally.",{"type":177,"children":248},[249],{"type":123,"text":250},"Expensive Updates and Deletes: Modifying existing data in ClickHouse is a heavy operation. Updates and deletes are implemented as \"mutations\" (ALTER TABLE ... UPDATE/DELETE), which rewrite entire data parts asynchronously in the background. They are not intended for high-frequency, low-latency transactional updates.",{"type":177,"children":252},[253],{"type":123,"text":254},"Schema Design Sensitivity: To achieve optimal performance, schemas must be carefully designed. ClickHouse performs best with denormalized tables (wide tables) or star schemas. While it supports joins, complex multi-way joins of massive tables can be highly resource-intensive and slower compared to specialized distributed SQL engines like Trino.",{"type":177,"children":256},[257],{"type":123,"text":258},"Resource Consumption: A single complex query in ClickHouse can easily consume all available CPU and memory resources on a node to return results as quickly as possible. While this behavior is intentional, it requires careful concurrency management and resource profiling in multi-tenant environments.",{"type":177,"children":260},[261],{"type":123,"text":262},"Operational Complexity: Managing a large, distributed ClickHouse cluster with replication, sharding, and coordination (via ClickHouse Keeper or ZooKeeper) requires significant operational expertise, particularly when handling node failures, schema migrations, or cluster rebalancing.",{"id":264,"type":265,"title":266,"content":267},59,"use-cases","Suitable Use Cases",[268,272],{"type":120,"children":269},[270],{"type":123,"text":271},"ClickHouse excels in scenarios that require fast, interactive querying over massive, append-only datasets:",{"type":173,"format":174,"children":273},[274,278,282,286,290],{"type":177,"children":275},[276],{"type":123,"text":277},"Web and Product Analytics: Tracking user behavior, clickstreams, page views, and session data in real time. ClickHouse allows product teams to run complex funnel analyses, cohort retention queries, and user segmentation on billions of events with sub-second response times.",{"type":177,"children":279},[280],{"type":123,"text":281},"Application and Infrastructure Monitoring (APM): Storing and analyzing logs, metrics, and traces. ClickHouse serves as an efficient backend for observability platforms, enabling engineers to search through terabytes of log data and generate real-time dashboards.",{"type":177,"children":283},[284],{"type":123,"text":285},"IoT and Sensor Telemetry: Ingesting high-frequency data streams from IoT devices, smart meters, or industrial sensors. ClickHouse's high write throughput and efficient compression make it ideal for storing long-term historical telemetry while keeping it accessible for instant analysis.",{"type":177,"children":287},[288],{"type":123,"text":289},"Ad Tech and Real-Time Bidding: Analyzing ad impressions, clicks, conversions, and bidding patterns to optimize campaigns and detect fraud in real time.",{"type":177,"children":291},[292],{"type":123,"text":293},"Financial Market Analysis: Storing tick-by-tick market data, order books, and historical transaction records for quantitative analysis, risk management, and algorithmic trading backtesting.",{"id":295,"type":296,"title":297,"content":298},60,"when-not-to-use","When Not to Use It",[299,303],{"type":120,"children":300},[301],{"type":123,"text":302},"ClickHouse should not be selected for workloads that deviate from its core analytical design:",{"type":173,"format":174,"children":304},[305,309,313,317],{"type":177,"children":306},[307],{"type":123,"text":308},"Online Transaction Processing (OLTP): If your application requires high-frequency, low-latency point reads, writes, and updates (such as user profile management, shopping carts, or financial ledgers requiring strict ACID guarantees), use a traditional relational database like PostgreSQL or MySQL.",{"type":177,"children":310},[311],{"type":123,"text":312},"Document or Key-Value Storage: For unstructured document storage or simple key-value lookups where analytical aggregation is not required, dedicated systems like MongoDB, Elasticsearch, or Redis are more appropriate.",{"type":177,"children":314},[315],{"type":123,"text":316},"Highly Normalized Schemas with Frequent Joins: If your data model relies heavily on complex, deeply nested joins across dozens of normalized tables, and denormalization is not feasible, ClickHouse may suffer from high memory consumption and degraded performance.",{"type":177,"children":318},[319],{"type":123,"text":320},"Low-Resource Environments: ClickHouse is designed to scale up and utilize hardware aggressively. Running it on extremely constrained virtual machines or edge devices with minimal CPU and RAM will not yield the performance benefits that justify its operational overhead.",[322,327,332],{"id":323,"label":324,"url":325,"kind":326},28,"ClickHouse Official Website","https://clickhouse.com","official",{"id":328,"label":329,"url":330,"kind":331},29,"ClickHouse Documentation","https://clickhouse.com/docs","docs",{"id":333,"label":334,"url":335,"kind":336},30,"ClickHouse GitHub Repository","https://github.com/ClickHouse/ClickHouse","repo",[],[339,342,345],{"id":323,"question":340,"answer":341},"Is ClickHouse a relational database?","Yes, ClickHouse is a relational database management system that supports SQL, but it is structured as a column-oriented database optimized for analytical queries rather than transactional processing.",{"id":328,"question":343,"answer":344},"How does ClickHouse handle updates and deletes?","ClickHouse does not support traditional, low-latency transactional updates and deletes. Instead, it uses \"mutations\" via the ALTER TABLE ... UPDATE/DELETE syntax, which are heavy, asynchronous background operations. For real-time deduplication or updates, engines like ReplacingMergeTree or CollapsingMergeTree are typically used.",{"id":333,"question":346,"answer":347},"Do I need ZooKeeper to run a distributed ClickHouse cluster?","Historically, ZooKeeper was required to coordinate replication and distributed DDL operations. Today, ClickHouse provides ClickHouse Keeper, an integrated, C++ based alternative that is fully compatible with the ZooKeeper protocol but consumes fewer resources and is easier to manage.",{"pagination":349},{"page":106,"pageSize":350,"pageCount":106,"total":106},25]