Choosing between ClickHouse^® and YDB often comes down to a single question: are you building analytics or transactions? Both databases emerged from Yandex's engineering teams and share Russian origins, but they solve fundamentally different problems with different performance characteristics.

This guide compares their architectural approaches, ingestion capabilities, query performance, scaling models, and operational requirements. You'll learn when each database makes sense for your workload and how their design tradeoffs affect real-world applications.

Architectural design and execution model

ClickHouse^® is a columnar OLAP database built for analytical queries, while YDB is a distributed SQL database designed for OLTP workloads and transactional integrity. OLAP stands for Online Analytical Processing, which means running complex queries over large datasets to generate reports and insights. OLTP stands for Online Transaction Processing, which handles individual record operations like inserts, updates, and deletes with strict consistency guarantees.

The key difference comes down to purpose: ClickHouse^® excels at scanning millions of rows to calculate aggregations, while YDB prioritizes maintaining data consistency across distributed transactions. This fundamental split in design philosophy affects everything from how data is stored on disk to how queries are executed.

Columnar storage vs hybrid row-column

ClickHouse^® stores each column separately on disk. When you run a query that only needs three columns from a table with fifty fields, ClickHouse^® reads just those three columns instead of scanning entire rows. This reduces disk I/O by orders of magnitude for analytical queries.

YDB uses a hybrid approach that stores transactional data in rows and can optionally use columnar storage for analytics. Row storage keeps all fields for a single record together, which works well for fetching complete records but wastes I/O when you only need a few columns.

• Compression benefits: Similar values stored together compress better than mixed data types, often achieving 10-20x compression in ClickHouse^®
• Query patterns: Analytical queries typically touch few columns but many rows, making columnar storage ideal
• Write performance: Row storage handles individual record updates faster, while columnar storage optimizes for bulk inserts

Vectorized query engine internals

ClickHouse^® processes data in batches using SIMD instructions, which let the CPU perform the same operation on multiple values simultaneously. A single CPU core can process millions of rows per second using this vectorized approach.

YDB uses row-by-row processing optimized for transactional consistency. Each operation is handled individually to maintain ACID guarantees, which stands for Atomicity, Consistency, Isolation, and Durability. While this ensures data integrity, it processes fewer rows per second during analytical scans.

The performance gap shows up most clearly in aggregations. Calculating a SUM across 100 million rows takes milliseconds in ClickHouse^® but seconds in row-oriented databases because of how the query engine processes data.

Compression codecs and disk IO

ClickHouse^® lets you specify different compression codecs for each column based on data characteristics. LZ4 provides fast decompression for frequently accessed data, while ZSTD achieves higher compression ratios for cold storage. Specialized codecs like Delta work well for incrementing sequences, and DoubleDelta handles time-series data efficiently.

YDB applies compression at the storage layer without per-column control. You get compression, but you can't tune it based on whether a column contains timestamps, user IDs, or event descriptions.

Better compression means lower storage costs and faster queries, since reading less data from disk directly improves query latency.

Streaming ingestion and real-time data freshness

Both databases handle continuous data ingestion, but they prioritize different guarantees. ClickHouse^® focuses on high-throughput batch ingestion with data available for queries within seconds. YDB ensures each write is confirmed with transactional consistency before acknowledging success.

The tradeoff is throughput versus guarantees. ClickHouse^® can ingest millions of events per second, while YDB provides stronger consistency at lower ingestion rates.

Kafka and CDC connectors

ClickHouse^® includes a native Kafka engine that reads directly from Kafka topics. You define the Kafka connection in your table schema, and ClickHouse^® continuously pulls data in the background. The integration supports JSON, Avro, Protobuf, and other formats without additional parsing.

YDB connects to Kafka through its Change Data Capture capabilities, which typically requires additional infrastructure components. The setup focuses on maintaining transactional integrity during ingestion rather than maximizing throughput.

For applications streaming millions of events per second from Kafka, ClickHouse^®'s native engine handles the load with straightforward configuration. YDB works better when you need guaranteed transaction ordering and consistency for each ingested record.

Exactly-once semantics and ordering

Exactly-once semantics guarantee that each message gets processed and stored one time, even if there are failures or retries. Without this guarantee, you might see duplicate records or missing data after network issues.

ClickHouse^® achieves exactly-once delivery through idempotent inserts when using the Kafka engine with proper configuration. The system prioritizes throughput over strict ordering within partitions, which means events might appear in slightly different orders than they arrived.

YDB wraps each write in a transaction that either fully succeeds or fully fails. This prevents partial writes and maintains strict ordering, but the transactional overhead reduces maximum ingestion speed.

Late-arriving events handling

Late-arriving events happen when data shows up out of chronological order. A mobile app might queue events offline and send them hours later when connectivity returns.

ClickHouse^® inserts late arrivals into the appropriate partition based on their timestamp. Since ClickHouse^® uses immutable data parts that merge periodically, late events get incorporated during the next merge operation. This means your data stays organized by time, but there's a brief delay before late arrivals appear in query results.

YDB can insert late-arriving data directly into the correct position because of its row-oriented storage. However, this flexibility comes with higher write amplification, where updating sorted data requires rewriting adjacent records.

Query latency and throughput benchmarks

Performance comparisons depend on workload characteristics, data volume, and hardware configuration. Rather than citing specific numbers, this section focuses on query types where each database performs best.

Understanding these patterns helps you predict performance for your use case.

Benchmark setup and dataset size

Fair comparisons require identical hardware, dataset sizes, and query patterns. The ClickBench benchmark uses a 100 million row clickstream dataset on standardized hardware to compare analytical databases, with ClickHouse^® storing it in just 9.26 GiB.

Dataset characteristics matter as much as size. High-cardinality columns, wide tables with many fields, and complex joins all affect performance differently. A dataset with 100 columns and a billion rows behaves differently than one with 10 columns and the same row count.

Real-world performance varies from benchmark results based on your specific schema, query patterns, and data distribution.

Analytical aggregation results

ClickHouse^® consistently outperforms YDB on analytical aggregations across large datasets. Queries with GROUP BY, SUM, AVG, and COUNT operations that scan millions of rows complete faster in ClickHouse^® because of columnar storage and vectorized execution.

• Simple aggregations: ClickHouse^® handles full table scans with aggregations 10-100x faster than row-oriented databases
• Time-series queries: Date partitioning and specialized time functions optimize temporal analysis
• Complex joins: Multi-table analytical joins execute more efficiently with columnar data

YDB performs better on queries requiring strong consistency or mixing updates with reads. The transactional model adds overhead that becomes noticeable in pure analytical workloads.

High-concurrency read tests

Concurrency measures how many simultaneous queries a database handles while maintaining acceptable latency. This matters for user-facing applications where hundreds of users might query the database at once.

ClickHouse^® handles high read concurrency well because queries don't lock data and multiple queries can scan different columns in parallel. However, each query consumes CPU and memory, so there are practical limits based on query complexity and available resources.

YDB's architecture handles thousands of concurrent point lookups efficiently but struggles with many concurrent analytical scans. The database is optimized for many users performing individual record operations rather than complex aggregations.

Scaling, replication, and fault tolerance

Both databases scale horizontally by adding nodes, but their approaches to data distribution and consistency differ significantly. ClickHouse^® uses manual sharding with eventual consistency, while YDB automatically partitions data with strong consistency guarantees.

Sharding strategy and rebalancing

ClickHouse^® uses manual sharding where you define how data distributes across nodes using a sharding key. The Distributed table engine routes queries to appropriate shards and merges results. This gives you explicit control over data placement but requires planning when adding nodes.

YDB automatically partitions data and rebalances partitions when you add or remove nodes. The automation reduces operational overhead but gives you less control over which data lives on which nodes.

Adding capacity in ClickHouse^® requires resharding existing data, which can take hours or days for large datasets. YDB rebalances automatically in the background, though this can temporarily affect query performance during rebalancing.

Consensus and consistency models

The CAP theorem states that distributed systems can provide at most two of three guarantees: Consistency, Availability, and Partition tolerance. ClickHouse^® and YDB make different tradeoffs here.

YDB uses the Raft consensus algorithm to maintain strong consistency across replicas. Every write is acknowledged by a majority of replicas before confirmation, ensuring all nodes have identical data. This provides immediate consistency but adds latency to each write operation.

ClickHouse^® relies on eventual consistency for replicated tables. Writes confirm quickly to clients, and replication happens asynchronously in the background. Replicas might temporarily have different data, but they converge within seconds.

Disaster recovery RPO/RTO

Recovery Point Objective (RPO) measures how much data you can afford to lose in a disaster, while Recovery Time Objective (RTO) measures how quickly you restore service.

ClickHouse^® supports continuous backups to S3 with RPO measured in minutes based on your backup schedule. Restoration involves replaying backups and rebuilding tables, with RTO typically measured in hours for large datasets.

YDB's synchronous replication provides lower RPO because data is replicated before write confirmation. RTO is also lower because replicas can immediately take over if the primary fails.

SQL dialect, APIs, and developer workflow

Integration ease depends on SQL compatibility, API design, and available tooling. Both databases support SQL but with different coverage and extensions.

ANSI SQL coverage and extensions

ClickHouse^® implements most of the ANSI SQL standard with extensions for analytical functions. Window functions, CTEs (Common Table Expressions), and array operations extend beyond standard SQL. However, ClickHouse^® intentionally omits or modifies some features like UPDATE and DELETE to maintain analytical performance.

YDB provides broader ANSI SQL compatibility focused on transactional operations. Standard DDL (Data Definition Language) and DML (Data Manipulation Language) operations work as expected for developers familiar with PostgreSQL or MySQL.

Applications migrating from traditional databases will find YDB's SQL more familiar, while applications focused on analytics benefit from ClickHouse^®'s specialized functions.

HTTP JSON endpoints and gRPC

ClickHouse^® exposes an HTTP interface that accepts SQL queries as POST requests and returns results in JSON, CSV, or other formats. You can query ClickHouse^® from any language without database-specific drivers.

YDB uses gRPC for client communication, which requires language-specific client libraries. The gRPC approach provides better performance for high-frequency operations but adds setup complexity.

Local dev loop with Tinybird CLI

Tinybird provides a CLI that lets you develop ClickHouse^® queries locally and deploy them as APIs without managing infrastructure. You test queries against local data using tb dev before pushing to production with tb deploy.

Changes to queries are versioned as code in .pipe files, making it easy to review and roll back changes. This workflow eliminates the gap between local development and production deployment.

Operational overhead and tooling

Maintaining a database in production includes monitoring, upgrades, security, and troubleshooting. Both databases offer tooling, but the maturity varies.

Observability and metrics exports

ClickHouse^® exposes detailed system tables that track query performance, resource usage, and cluster health. You query these tables like any other data, making it easy to build custom monitoring dashboards.

YDB provides metrics through its monitoring interface and exports metrics to Prometheus. The metrics cover query latency, throughput, and resource utilization.

Both databases integrate with standard monitoring tools like Grafana and Datadog.

Zero-downtime upgrades

ClickHouse^® supports rolling upgrades where you update one replica at a time while others continue serving queries. This works well for clusters with multiple replicas but requires planning for single-node deployments.

YDB's distributed architecture allows node-by-node upgrades with automatic failover. The consensus protocol ensures queries continue working even as individual nodes restart.

Schema changes in ClickHouse^® are generally non-blocking for read queries but can affect write performance during migrations. YDB provides online schema evolution that applies changes without downtime.

Security and access control

Both databases support role-based access control (RBAC), which lets you define users, roles, and permissions for different database objects. RBAC allows granting specific users access to certain tables while restricting others.

ClickHouse^® provides user authentication through configuration files or SQL commands, with support for LDAP and Kerberos integration. Encryption in transit uses standard TLS connections.

YDB includes built-in authentication and authorization with fine-grained permissions at the table and row level. This makes multi-tenant applications easier to implement where different users see different data.

Cost considerations at scale

Total cost of ownership includes infrastructure, storage, and operational overhead. The cost structure differs between analytical and transactional databases.

Storage footprint and compression ratio

ClickHouse^®'s columnar compression typically achieves 10-20x compression on analytical data. A 1TB dataset might compress to 50-100GB on disk, directly reducing storage costs.

YDB's hybrid storage model compresses less aggressively, typically achieving 3-5x compression. You'll need more storage capacity for the same amount of data.

For time-series or log data with repetitive patterns, ClickHouse^®'s specialized compression codecs like Delta and DoubleDelta can exceed 50x compression on numeric sequences.

Compute per query vs always-on nodes

ClickHouse^®'s resource usage scales with query complexity and data volume. Simple queries on indexed data use minimal CPU, while complex aggregations can max out all available cores.

YDB maintains background processes for consensus and replication even when idle. You're paying for compute capacity whether or not you're running queries.

For workloads with unpredictable query patterns, ClickHouse^®'s pay-per-query model can be more cost-effective than maintaining always-on transactional infrastructure.

Managed service vs self-hosted TCO

Self-hosting either database requires expertise in Linux administration, storage management, backup strategies, and performance tuning. The operational cost often exceeds infrastructure costs for small teams.

Managed services like Tinybird for ClickHouse^® or Yandex Cloud for YDB handle infrastructure, monitoring, backups, and upgrades. This shifts costs from operational overhead to service fees.

For development teams focused on building applications rather than managing databases, managed services typically provide better time-to-value despite higher per-unit costs.

When to choose ClickHouse^® or YDB

The right database depends on your workload characteristics, consistency requirements, and team capabilities. Neither database is universally better; they're optimized for different use cases.

Pure analytical workloads

ClickHouse^® excels when your primary use case is running analytical queries over large historical datasets. This includes business intelligence dashboards, user behavior analytics, and log analysis.

If you're building a product analytics platform, real-time monitoring dashboard, or data warehouse, ClickHouse^® delivers the query performance users expect.

Mixed OLTP/OLAP scenarios

YDB handles both transactional and analytical workloads in a single database. If your application processes transactions and runs analytics on the same data without moving it between systems, YDB's hybrid model reduces architectural complexity.

However, this flexibility comes with tradeoffs. YDB won't match ClickHouse^®'s analytical performance or a pure OLTP database's transactional throughput. You're optimizing for operational simplicity rather than peak performance in either workload type.

Low-latency API backends

For user-facing APIs that return query results in milliseconds, both databases can work depending on query patterns.

ClickHouse^® handles analytical queries with sub-second latency even on large datasets, making it suitable for dashboards and reporting APIs. Point lookups and updates are slower because of the columnar storage model.

YDB provides faster point lookups and transactional operations, making it better for APIs that fetch individual records or perform updates. Analytical aggregations are slower than ClickHouse^®.

Faster time-to-value with Tinybird managed ClickHouse^®

Tinybird provides managed ClickHouse^® infrastructure that eliminates the operational complexity of running clusters yourself. You can start building data products in minutes rather than weeks spent on infrastructure setup.

The platform handles automatic scaling, monitoring, backups, and performance optimization so your team can focus on building features instead of managing databases. Tinybird also provides API endpoints that turn your SQL queries into production-ready REST APIs with authentication and rate limiting built in.

Sign up for a free Tinybird account to start building with managed ClickHouse^® without infrastructure work.

FAQs about ClickHouse^® and YDB

How do secondary indexes differ between ClickHouse^® and YDB?

ClickHouse^® uses sparse indexes and skip indexes that store min/max values for data blocks rather than indexing every row. This reduces index size but means point lookups scan multiple blocks. YDB supports traditional B-tree secondary indexes that provide faster point lookups at the cost of higher storage overhead and write amplification.

Can I run both databases in a multi-cloud architecture?

Yes, both databases support multi-cloud deployments. YDB offers native multi-region replication with automatic failover, making it easier to maintain consistency across clouds. ClickHouse^® requires manual configuration of replication between regions, typically using the Distributed table engine or external tools, but gives you more control over data placement and query routing.

What are the licensing terms for commercial use?

Both ClickHouse^® and YDB use the Apache 2.0 license, which allows free commercial use without restrictions. You can modify the source code and deploy it in production without licensing fees. Commercial support and managed services are available from various vendors, including Tinybird for ClickHouse^® and Yandex Cloud for YDB.

/