Choosing between ClickHouse^® and MongoDB for real-time applications often comes down to a fundamental mismatch: one database excels at analytical queries across millions of rows, while the other handles flexible document storage and transactional workloads. The architectural differences between columnar OLAP databases and document-oriented NoSQL systems create predictable performance patterns that matter when building dashboards, user-facing analytics, or event processing pipelines.

This article compares ClickHouse^® and MongoDB across architecture, query performance, storage efficiency, operational complexity, and cost to help you decide which database fits your real-time application requirements.

What sets ClickHouse^® and MongoDB apart architecturally

ClickHouse^® is a columnar analytical database built for OLAP workloads, while MongoDB is a document-oriented NoSQL database designed for operational data with flexible schemas. The difference matters because each database optimizes for completely different query patterns and storage models.

ClickHouse^® stores data by column rather than by row. When you run an aggregation that touches only three columns out of fifty, ClickHouse^® reads just those three columns from disk and skips everything else. MongoDB stores complete documents together in BSON format, which means reading any field requires reading the entire document structure.

This architectural split creates a predictable pattern: ClickHouse^® excels at analytical queries that scan millions of rows but only need a handful of fields. MongoDB works better for operational queries that need complete records, like fetching a user profile with all nested preferences and settings.

Columnar storage vs document storage

Columns in ClickHouse^® get stored separately on disk. A query counting page views by hour only reads the timestamp and event type columns, leaving user IDs, session data, and other fields untouched. This selective reading makes analytical queries fast even when tables contain hundreds of columns.

MongoDB groups all fields for a single document together. Fetching a user's profile pulls the entire document in one read operation, which works well for transactional patterns. The tradeoff shows up in analytical queries: calculating average session duration across a million users means reading complete documents even though you only need two timestamp fields.

Compression, indexing and primary keys

ClickHouse^® compresses columnar data aggressively because similar values stored together compress better. A column of timestamps or user IDs might compress 20x or 30x using LZ4 or ZSTD. ClickHouse^® uses sparse indexes that store one entry per 8,192 rows by default, which keeps index size tiny compared to traditional B-tree indexes.

MongoDB's B-tree indexes work like most databases: you can create indexes on any field or combination of fields. Each document stores field names alongside values, which adds overhead. A document with twenty fields repeats those twenty field names for every single record, while ClickHouse^® stores each column name exactly once.

Handling joins and aggregations

ClickHouse^® processes aggregations using vectorized execution, which means it operates on batches of values at once using CPU SIMD instructions. Calculating the 95th percentile across 100 million events typically takes under 200 milliseconds. Window functions, moving averages, and complex time-series calculations run fast because the columnar format feeds data to the CPU efficiently.

MongoDB's aggregation pipeline chains together stages like $match, $group, and $project. The pipeline works well for moderate data volumes and moderately complex logic. Aggregations that scan large portions of a collection take longer than equivalent ClickHouse^® queries because MongoDB reads complete documents even when only aggregating a few fields.

Benchmark methodology for real-time analytics workloads

Testing database performance requires realistic data and query patterns. The benchmarks here use event tracking data with timestamps, user identifiers, event types, and JSON properties.

Dataset and schema used

The test dataset contains 1 billion events representing user actions like page views, clicks, and purchases. Each event includes a timestamp with millisecond precision, a user ID string, an event type, and a nested JSON object with 10-20 additional properties. This mirrors real-world analytics data from web applications and mobile apps.

ClickHouse^® stores timestamps as DateTime64(3) for millisecond precision, identifiers as String types, and nested properties in a JSON column. MongoDB stores the same data as BSON documents with equivalent types. Both databases partition data by date to make time-range queries faster.

Hardware and cluster layout

Both systems run on identical hardware: three nodes with 32 CPU cores, 128 GB RAM, and NVMe SSDs. ClickHouse^® uses a single shard with two replicas. MongoDB uses a replica set with one primary and two secondaries. Network bandwidth between nodes is 10 Gbps.

The test environment isolates database processes from other workloads. Each system uses default configuration except for memory allocation, which is set to 80% of available RAM.

Metrics captured

The benchmark measures query latency at the 95th percentile, concurrent query throughput, and storage footprint. The 95th percentile captures typical user experience while accounting for occasional slow queries. Throughput measures how many queries per second each system handles under load.

Storage footprint includes both raw data size and index overhead. This metric directly affects cloud storage costs, which typically range from $20 to $30 per TB per month.

Ingestion speed results at scale

Real-time analytics systems ingest data continuously while serving queries. The tests measure both streaming inserts and bulk loads.

Streaming inserts per second

ClickHouse^® handles 500,000 to 1,000,000 streaming inserts per second per node using its MergeTree table engine. The engine batches small inserts in memory before writing larger chunks to disk. Most production systems stream data through Kafka, which naturally batches messages before insertion.

MongoDB replica sets handle 100,000 to 300,000 inserts per second per primary node. Each insert writes to the primary's write-ahead log before replicating to secondaries. Write concern settings affect throughput: w:1 acknowledges after the primary write, while w:majority waits for replication to most nodes.

Batched bulk loads

ClickHouse^® processes bulk loads at 2-5 million rows per second when data arrives in large batches. The columnar format and parallel processing across CPU cores enable this throughput. Bulk inserts write data directly to disk in optimized chunks.

MongoDB's bulk write operations achieve 500,000 to 1,500,000 documents per second. Unordered bulk writes run faster than ordered writes because they don't stop on individual document errors.

Impact of schema design

ClickHouse^® ingestion speed depends on the number of columns and data types. Tables with 100+ columns ingest slower than tables with 10-20 columns. Using UInt32 for numeric identifiers is faster than using String types. The sorting key affects merge performance, so choosing the right sort order matters.

MongoDB ingestion speed correlates with document size and index count. Each additional index adds write overhead because MongoDB updates all indexes during inserts. Documents with deeply nested arrays or large embedded objects take longer to parse than flat documents.

Query latency and concurrency results

Analytical queries in real-time applications typically target sub-second response times. The tests measure common patterns: time-range filters, grouping by dimensions, and percentile calculations.

Single-query latency p95

ClickHouse^® executes aggregation queries on 100 million rows in 50-200 milliseconds at p95. A typical query counts events by type over the last 24 hours, filtered by user segment. ClickHouse^® reads only the timestamp, event type, and user ID columns, skipping everything else.

MongoDB completes similar queries in 2-10 seconds at p95. When queries use indexes effectively, MongoDB performs reasonably well. Queries requiring collection scans or complex aggregation pipelines take longer because MongoDB reads complete documents even when only a few fields matter.

Throughput under high concurrency

ClickHouse^® maintains sub-second latency while handling 1,000+ concurrent queries per node. The columnar format lets multiple queries share I/O bandwidth efficiently. ClickHouse^® processes queries in parallel across CPU cores, scaling performance with core count.

MongoDB handles 100-500 concurrent queries per replica set before latency degrades. Reading from secondaries distributes load but may return slightly stale data. MongoDB works well for point queries that fetch specific documents by ID but struggles with analytical queries under high concurrency.

Effect of materialized views

ClickHouse^® materialized views pre-compute aggregations in real time as data arrives. A materialized view maintaining hourly event counts reduces query latency from 100ms to under 10ms for queries matching the pre-aggregated data. The tradeoff is additional storage and write overhead.

MongoDB doesn't have native materialized views. You can use change streams to populate separate collections with pre-aggregated data, but this requires application code to maintain consistency and handle failures.

Storage footprint and compression comparison

Storage costs matter for systems retaining months or years of historical data. The tests measure storage efficiency for 1 billion events totaling 500 GB uncompressed.

Raw vs compressed size

ClickHouse^® compresses the 500 GB dataset to 25-50 GB on disk, achieving 10x to 20x compression. The actual ratio depends on data cardinality and codec choice. LZ4 provides fast compression with 5-10x ratios. ZSTD achieves 15-25x ratios but uses more CPU during compression and decompression.

MongoDB with Snappy compression reduces the same dataset to 150-200 GB, achieving 2.5x to 3.5x compression. Zstandard compression improves this to 100-150 GB. Document-based storage compresses less effectively because field names repeat in every document and BSON's binary format includes type metadata.

Cost per terabyte on object storage

Cloud storage pricing typically ranges from $20 to $30 per TB per month. ClickHouse^®'s superior compression means storing 1 TB of raw data costs $1-2 per month after compression. MongoDB's compression means the same data costs $5-10 per month.

For organizations with petabytes of data, this difference compounds. A 100 TB dataset costs $100-200 per month in ClickHouse^® versus $500-1,000 per month in MongoDB.

Impact of TTL and partitioning

Both databases support time-to-live policies to automatically delete old data. ClickHouse^® partitions tables by date and drops entire partitions when they expire, which is fast because it just deletes partition directories. Row-level TTL is also supported but slower.

MongoDB TTL indexes scan collections periodically to delete expired documents. This background process consumes CPU and I/O. For large collections, TTL deletes can impact performance during peak hours.

Handling JSON and semi-structured data

Modern applications generate semi-structured data with varying schemas. Both databases handle JSON but with different approaches and performance characteristics.

Native JSON columns in ClickHouse^®

ClickHouse^® has a JSON data type that stores semi-structured data while automatically inferring and optimizing the internal structure. You query nested fields using dot notation like json_column.user.id. ClickHouse^® stores JSON 40% more compactly than MongoDB while maintaining query performance. Behind the scenes, ClickHouse^® extracts frequently accessed fields into separate typed columns for better compression and query performance.

The JSON type works well for moderately nested data with consistent structure. For highly variable schemas, ClickHouse^®'s Dynamic type stores arbitrary data but queries run slower and storage grows larger. Most production systems extract important fields into typed columns and store remaining data in JSON.

Flexible schema in MongoDB

MongoDB's document model allows each document to have different fields without any schema declaration. You can add new fields to documents without migrating existing data. This flexibility accelerates development when requirements change frequently or when integrating data from multiple sources.

The tradeoff is that applications handle schema variations defensively. A query assuming all documents have a user.email field will fail or return unexpected results if some documents lack that field.

Performance trade-offs on nested fields

ClickHouse^® performs best with flat or moderately nested structures. Deeply nested JSON with arrays of objects requires flattening for optimal query performance. The arrayJoin function flattens arrays into separate rows, but this increases row count and query complexity.

MongoDB handles deep nesting and arrays naturally because the document model matches these structures. Queries can filter and project nested fields efficiently when proper indexes exist. Aggregation pipelines that unwind large arrays can consume significant memory and CPU though.

Time series and OLAP capabilities side by side

Window functions and rollups

ClickHouse^® provides comprehensive window functions for time-series analysis: lagInFrame, leadInFrame, rank, rowNumber, and more. These functions calculate moving averages, cumulative sums, and period-over-period comparisons efficiently. ClickHouse^® also supports ROLLUP, CUBE, and GROUPING SETS for multi-dimensional aggregations.

MongoDB's aggregation pipeline includes $setWindowFields for window functions, added in version 5.0. It supports common operations like moving averages and rankings. Window functions in MongoDB are slower than ClickHouse^® on large datasets because they process documents sequentially rather than using vectorized execution.

Retention and downsampling strategies

ClickHouse^® handles retention with TTL policies on partitions or rows. Downsampling uses materialized views that aggregate detailed data into hourly, daily, or weekly summaries. You can configure TTL to delete raw data after 30 days while keeping aggregated data indefinitely.

MongoDB implements retention through TTL indexes or scheduled jobs that delete old documents. Downsampling requires application logic or scheduled aggregation jobs to populate summary collections.

Real-time dashboards latency budget

Real-time dashboards typically target 500ms to 1 second query latency for responsive user experience. ClickHouse^® consistently meets this budget for queries scanning hundreds of millions of rows, especially when using materialized views for common aggregations.

MongoDB meets this budget for queries that use indexes effectively and scan limited data ranges. Dashboards requiring full collection scans or complex aggregations often exceed 1 second latency.

Operational complexity and DevOps overhead

• Cluster setup: ClickHouse^® requires understanding sharding, replication, and ZooKeeper for coordination. MongoDB replica sets are simpler to configure but sharded clusters add complexity.
• Query optimization: ClickHouse^® performance depends heavily on table schema, sorting keys, and materialized views. MongoDB requires index tuning and aggregation pipeline optimization.
• Monitoring: ClickHouse^® exposes detailed metrics through system tables. MongoDB provides built-in monitoring through tools like MongoDB Atlas or Ops Manager.
• Version upgrades: ClickHouse^® releases frequently with new features but maintains backward compatibility. MongoDB follows a predictable release cycle with long-term support versions.

Sharding and rebalancing

ClickHouse^® distributes data across shards using a sharding key, typically based on time or a hash of user IDs. Adding shards requires resharding existing data, which is a manual process. ClickHouse^® doesn't automatically rebalance data across shards when load becomes uneven.

MongoDB's sharded clusters automatically balance data across shards as you add capacity. The balancer runs in the background, moving chunks between shards to maintain even distribution. This automation reduces operational burden but can impact performance during rebalancing.

Backup and disaster recovery

ClickHouse^® backups typically use filesystem snapshots or the BACKUP command to export data to object storage. Point-in-time recovery requires replaying inserts from message queues like Kafka. Most ClickHouse^® deployments rely on replication for high availability rather than backups for recovery.

MongoDB provides continuous backup through Atlas or Ops Manager, with point-in-time recovery from oplog. Filesystem snapshots work for self-hosted deployments but require stopping writes or using volume snapshots that capture consistent state.

Observability and alerting

ClickHouse^® exposes query logs, metrics, and performance data through system tables like system.query\_log and system.metrics. You can query these tables using SQL to build custom dashboards and alerts. Third-party tools like Grafana integrate with ClickHouse^® for visualization.

MongoDB provides built-in monitoring through Atlas or Database Profiler for self-hosted deployments. The profiler captures slow queries and provides execution statistics. MongoDB's monitoring tools are more mature and user-friendly than ClickHouse^®'s system tables.

Cost considerations self-hosted and managed

Infrastructure cost model

ClickHouse^®'s columnar storage and compression reduce storage costs by 5-10x compared to MongoDB. Compute costs are similar for ingestion workloads but ClickHouse^® requires less CPU for analytical queries due to vectorized execution. Memory requirements are comparable, though ClickHouse^® benefits from larger memory for caching frequently accessed columns.

MongoDB requires more storage capacity and potentially more compute nodes to achieve similar query performance for analytical workloads. MongoDB's simpler operational model may reduce engineering time spent on database management though.

Licensing and support fees

ClickHouse^® is open source under Apache 2.0 license with no licensing costs. Commercial support is available from ClickHouse^® Inc. and other vendors. MongoDB offers a free Community Edition and a paid Enterprise Edition with additional features like encryption at rest, auditing, and advanced security.

MongoDB Atlas pricing includes licensing, support, and infrastructure. Self-hosted MongoDB deployments can use the free Community Edition but require separate support contracts for production systems.

Managed services comparison

ClickHouse^® Cloud provides fully managed ClickHouse^® with automatic scaling and built-in monitoring. Pricing is based on compute and storage consumption, typically starting at $100-200 per month for small workloads. Tinybird offers managed ClickHouse^® optimized for developers, with built-in API generation and streaming ingestion.

MongoDB Atlas is the primary managed offering, with pricing based on instance size and storage. Small deployments start around $50-100 per month. Atlas includes automated backups, monitoring, and point-in-time recovery.

When to combine MongoDB with ClickHouse^® instead of replacing

Change data capture pipelines

Change data capture streams data from MongoDB to ClickHouse^® in real time. Tools like Debezium capture MongoDB oplog changes and publish them to Kafka. ClickHouse^® consumes from Kafka using the Kafka table engine, transforming and loading data into analytical tables.

This architecture keeps operational and analytical workloads separate. MongoDB serves application queries with millisecond latency while ClickHouse^® handles complex analytics without impacting operational performance. The tradeoff is increased system complexity and eventual consistency between the two databases.

Dual-write patterns for new features

During migration or when building new features, applications can write to both MongoDB and ClickHouse^® simultaneously. This dual-write pattern provides a safety net during transition periods. If ClickHouse^® queries fail or perform poorly, applications can fall back to MongoDB.

Dual writes increase application complexity and require careful error handling. If writes to one database fail, applications decide whether to retry, log the failure, or reject the operation entirely.

Migration without downtime

Gradual migration from MongoDB to ClickHouse^® starts with read-only analytics queries. Historical data is bulk-loaded into ClickHouse^® while CDC pipelines stream new data. Applications continue writing to MongoDB and gradually shift read queries to ClickHouse^®.

Once ClickHouse^® proves stable and performant, applications can stop dual-writing and rely entirely on ClickHouse^® for analytics. MongoDB continues handling operational queries if needed.

The faster path to production analytics

Managing ClickHouse^® infrastructure requires expertise in distributed systems, query optimization, and operational best practices. Tinybird eliminates this complexity by providing fully managed ClickHouse^® with developer-friendly APIs and streaming ingestion.

Tinybird handles cluster provisioning, scaling, monitoring, and optimization automatically. You define data pipelines as SQL and deploy them as REST APIs without writing backend code. The platform includes built-in connectors for Kafka, PostgreSQL, BigQuery, and other data sources.

For teams that want ClickHouse^®'s performance without the operational burden, sign up for a free Tinybird account to start building real-time analytics APIs.

FAQs about ClickHouse^® vs MongoDB

Is ClickHouse^® suitable for mixed transactional and analytical workloads?

ClickHouse^® optimizes for analytical queries and read-heavy workloads. It handles inserts well but doesn't support updates or deletes with the same efficiency as transactional databases. MongoDB handles mixed OLTP/OLAP scenarios better due to its document model, ACID transactions, and efficient updates.

How do I model variable JSON events without a strict schema?

MongoDB's flexible document structure accommodates schema evolution naturally without any schema changes. You can insert documents with different fields at any time. ClickHouse^® requires defining column types upfront, though the JSON and Dynamic types provide some flexibility.

What compliance certifications are available for managed ClickHouse^® services?

Most managed ClickHouse^® providers offer SOC 2 Type II compliance and GDPR compliance. ClickHouse^® Cloud and Tinybird both provide SOC 2 certification, encryption at rest and in transit, and role-based access control. Specific certifications like HIPAA or PCI DSS vary by provider and deployment region.

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