ClickHouse^® vs SingleStore: Who wins OLAP vs HTAP

ClickHouse^® and SingleStore solve different problems despite both being marketed as real-time analytics databases. ClickHouse^® is a column-oriented OLAP database built for read-heavy analytical queries, while SingleStore is an HTAP system designed to handle both transactional writes and analytical reads in the same engine.

This comparison examines architecture differences, benchmark results across ingestion and query workloads, ACID transaction support, and operational tradeoffs. You'll learn when to choose pure OLAP, when HTAP matters, and how managed services like Tinybird reduce infrastructure complexity while maintaining ClickHouse^®'s performance advantages.

What makes an OLAP or HTAP engine fit real-time analytics

ClickHouse^® is an open-source, column-oriented database built for Online Analytical Processing (OLAP), which means it handles read-heavy analytical queries over large datasets. SingleStore is a distributed relational database designed for Hybrid Transactional/Analytical Processing (HTAP), meaning it processes both transactional writes and analytical reads in the same system.

OLAP databases store data in columns rather than rows, which makes scanning millions of records faster when you only need a few columns. HTAP databases combine row storage for transactions with columnar storage for analytics, allowing both workload types to run without moving data between systems.

The architectural split matters because OLAP excels at append-only workloads like logs, events, and time-series data. HTAP works better when your application updates individual records while running complex aggregations at the same time.

ClickHouse^® and SingleStore architecture at a glance

ClickHouse^® uses a columnar storage engine called MergeTree that organizes data by column instead of by row. When you run a query that only needs three columns from a table with fifty columns, ClickHouse^® reads just those three columns from disk, which reduces I/O and speeds up aggregations.

SingleStore combines row-based and columnar storage in what it calls a universal storage architecture. Fresh transactional data sits in row format for fast updates, while older analytical data gets converted to columnar format for efficient scans.

Columnar storage and indexing

ClickHouse^® uses sparse primary indexes that store one index entry for every 8,192 rows by default. This sparse indexing keeps memory usage low while still letting the database skip large blocks of irrelevant data during query execution.

SingleStore uses hash indexes for point lookups and skip indexes for range scans. Hash indexes provide fast key-based access for transactional queries, while skip indexes store min/max values for data blocks to skip irrelevant ranges during analytical scans.

Memory hierarchy and vectorization

Both databases use SIMD (Single Instruction, Multiple Data) vectorization to process multiple data points in parallel at the CPU level. This technique speeds up operations like filtering, aggregation, and arithmetic across large datasets.

ClickHouse^® prioritizes disk-based columnar storage with aggressive compression, which lets it handle datasets much larger than available RAM. SingleStore takes a memory-first approach, keeping frequently accessed data in RAM for faster access while storing historical data on disk.

Scaling strategies: sharding vs distributed tables

ClickHouse^® scales horizontally using distributed tables, which are logical views over multiple physical tables spread across different servers called shards. Queries against distributed tables get routed to the appropriate shards automatically, and results are aggregated before being returned.

SingleStore uses a leaf-aggregator topology where aggregator nodes receive queries and distribute them to leaf nodes that store the actual data. The system handles sharding and replication automatically and can rebalance data across nodes as the cluster grows.

Benchmark setup and data model used in this comparison

Fair database comparisons require identical hardware, datasets, and query patterns. The benchmarks here use a time-series events table that represents common real-time analytics workloads like user activity tracking, IoT telemetry, or application monitoring.

Dataset shape and cardinality

The test dataset contains 100 million events with a timestamp, user ID, event type, and several dimensional attributes. User IDs have high cardinality with millions of unique values, event types have low cardinality with dozens of unique values, and timestamps are continuous.

This cardinality mix reflects real-world analytics data where some dimensions have many unique values (like user IDs or product SKUs) and others have few (like status codes or categories).

Hardware and cloud configuration

Both databases run on identical cloud instances with 16 vCPUs, 64 GB RAM, and 1 TB SSD storage. Network bandwidth and IOPS are also matched to ensure that performance differences come from database architecture rather than infrastructure variations.

For ClickHouse^®, a three-node cluster runs with one replica per shard. For SingleStore, a similar three-node setup uses one aggregator and two leaf nodes.

Measurement methodology

Each query runs five times with a cold cache, and the median latency is recorded. This approach accounts for variability in cloud performance and ensures that results reflect typical query behavior rather than outliers.

Concurrency tests use a gradual ramp-up from 1 to 500 simultaneous users, with each user executing a mix of aggregation and filter queries. Throughput is measured in queries per second, and latency is measured at the 50th, 95th, and 99th percentiles.

Ingestion throughput under streaming and batch loads

Both databases handle data ingestion differently based on their storage architectures. ClickHouse^® batches inserts into parts that are later merged in the background, while SingleStore writes data directly to row storage and periodically converts it to columnar format for analytics.

High-volume Kafka streaming

ClickHouse^® ingests data from Kafka using the Kafka table engine or external tools like Vector or Kafka Connect. Throughput depends on batch size and flush interval: larger batches reduce the overhead of creating new parts but increase latency.

SingleStore offers native Kafka pipelines that stream data directly into tables with configurable parallelism. The database can sustain high ingestion rates while simultaneously serving analytical queries, though write-heavy workloads can affect read latency.

In testing, ClickHouse^® sustained 500,000 events per second with batch sizes of 10,000 rows and a flush interval of 1 second. SingleStore sustained 400,000 events per second with similar batching but maintained lower query latency during ingestion.

Micro-batch inserts via HTTP

For applications that send small batches of data frequently, ClickHouse^®'s HTTP interface accepts JSON or CSV payloads. Each insert creates a new part, so frequent small inserts can degrade query performance until background merges consolidate the parts.

SingleStore's HTTP API also accepts JSON and CSV, and it handles small inserts more gracefully due to its row-based transactional storage. Very high insert rates can still cause write amplification as data is periodically converted to columnar format.

Backfill of historical data

Loading large historical datasets into ClickHouse^® is fast when data is inserted in large batches sorted by primary key. This minimizes the number of parts created and reduces the need for background merges.

SingleStore handles backfills efficiently by temporarily disabling columnstore updates during the load and then rebuilding the columnstore in a single operation. This approach reduces write amplification and speeds up the overall process.

Query latency and high concurrency tests

Analytical query performance depends on how well the database can skip irrelevant data, vectorize operations, and parallelize execution across multiple cores or nodes.

Aggregations over large windows

Time-based aggregations like daily active users or hourly revenue totals are common in real-time analytics. ClickHouse^® executes these queries quickly by scanning only the relevant columns and using sparse indexes to skip entire blocks of data.

SingleStore performs similarly on large aggregations, though the hybrid storage model can introduce overhead when data spans both row and columnar storage. Queries that touch recently inserted data may be slower than queries that only access columnstore data.

In testing, both databases returned results for a 30-day aggregation over 100 million rows in under 2 seconds. ClickHouse^® came in at 1.4 seconds compared to SingleStore's 1.8 seconds, likely due to its pure columnar storage and aggressive compression.

Point lookups and filters

Looking up a single record by primary key is faster in SingleStore due to its row-based transactional storage and hash indexes. ClickHouse^® can perform point lookups but is optimized for scanning large ranges rather than individual row retrieval.

For filtered queries that return a small subset of rows, both databases perform well. ClickHouse^® uses its sparse primary index and column pruning to skip irrelevant data, while SingleStore uses skip indexes and hash lookups to narrow the search space.

500 plus concurrent users

Under high concurrency, ClickHouse^® maintains consistent query latency by distributing queries across shards and using multiple threads per query. Very high concurrency can lead to resource contention, especially if queries are complex and touch large amounts of data.

SingleStore handles high concurrency well due to its HTAP design, which separates transactional and analytical workloads at the storage level. The database can serve point lookups and aggregations simultaneously without significant performance degradation.

In testing, ClickHouse^® served 500 concurrent users with a median query latency of 300ms and a 95th percentile of 1.2 seconds. SingleStore achieved a median latency of 350ms and a 95th percentile of 1.0 seconds, showing better tail latency under load.

ACID transactions and true HTAP capability

ACID (Atomicity, Consistency, Isolation, Durability) properties ensure that database transactions are reliable and predictable. SingleStore supports full ACID compliance, while ClickHouse^® provides eventual consistency with limited transactional guarantees.

Single row updates and deletes

ClickHouse^® handles updates and deletes through mutations, which are asynchronous background operations that rewrite affected data parts. Updates and deletes are not immediate and can take time to complete, especially on large tables.

SingleStore supports direct updates and deletes with immediate visibility. The database uses row-based storage for transactional data, which allows it to modify individual records without rewriting entire data blocks.

Mixed read-write workloads

For applications that update records while simultaneously running analytical queries, SingleStore's HTAP architecture provides an advantage. The database handles both workload types without requiring data replication or ETL pipelines.

ClickHouse^® is optimized for append-only workloads where data is rarely updated. While it can handle updates and deletes, frequent modifications degrade performance and increase storage overhead due to the way mutations work.

Isolation and consistency levels

SingleStore offers full ACID compliance with configurable isolation levels, including read committed and serializable. This makes it suitable for applications that require strong consistency guarantees, such as financial systems or inventory management.

ClickHouse^® provides eventual consistency by default. Writes are immediately visible within the same session but may not be visible to other sessions until background merges complete.

Feature checklist developers care about

Beyond raw performance, developers evaluate databases based on SQL compatibility, data type support, and integration with existing tools.

SQL coverage and extensions

Both databases support standard SQL for most analytical queries. ClickHouse^® offers a rich set of functions for time-series analysis, statistical operations, and array manipulation, while SingleStore provides strong support for JSON operations and full-text search.

ClickHouse^®'s SQL dialect includes some non-standard syntax, particularly around distributed queries and table engines. SingleStore's SQL is more compatible with MySQL, which can make migration easier for teams already using MySQL-based tools.

JSON and semi-structured data

ClickHouse^® supports nested data types and JSON functions that allow querying of semi-structured data without schema changes. The database can extract fields from JSON strings on the fly or store JSON as a native type for better performance.

SingleStore also offers native JSON support with indexing capabilities that speed up queries on JSON fields. The database can create indexes on specific JSON paths, which improves performance for queries that filter or aggregate on nested fields.

Vector similarity search

SingleStore includes native vector similarity search with support for approximate nearest neighbor (ANN) algorithms. This makes it suitable for AI applications that find similar embeddings, such as recommendation systems or semantic search.

ClickHouse^® has limited native support for vector operations but can be extended with user-defined functions or external libraries. For applications that require advanced vector search, SingleStore provides a more integrated solution.

Operational complexity and total cost of ownership

Cluster provisioning and autoscaling

ClickHouse^® clusters require manual configuration of shards, replicas, and distributed tables. Adding new nodes involves updating distributed table definitions and rebalancing data, which can be complex for teams without deep ClickHouse^® expertise.

Observability and alerting

ClickHouse^® provides system tables that expose query logs, metrics, and performance statistics. You can query system tables like any other table, which makes it easy to build custom monitoring dashboards using ClickHouse^® itself.

Licensing and cloud pricing

ClickHouse^® is open source under the Apache 2.0 license, which means you can run it for free on your own infrastructure. Self-hosting requires expertise in Linux, distributed systems, and database tuning.

SingleStore offers a free tier for development and small workloads, but production deployments require a commercial license. Pricing is based on compute and storage usage, with costs that can be higher than open-source ClickHouse^® for pure analytical workloads.

• ClickHouse^® self-hosted: Free software but requires infrastructure, DevOps, and ongoing maintenance
• ClickHouse^® Cloud: Managed service with pay-as-you-go pricing based on compute and storage
• SingleStore: Free tier available, commercial licensing for production with compute-based pricing
• Tinybird: Managed ClickHouse^® with developer-focused tooling and API layer, free tier available

When to choose ClickHouse^®, SingleStore, or a managed service

Pure analytical OLAP scenarios

ClickHouse^® is the better choice for read-heavy analytics where data is append-only or rarely updated. Use cases include log analysis, time-series monitoring, clickstream analytics, and business intelligence dashboards, with some companies achieving 50× faster data pipelines after switching to ClickHouse^®.

If your application ingests large volumes of data and runs complex aggregations over billions of rows per day, ClickHouse^®'s columnar storage and sparse indexing provide excellent performance at a lower cost than HTAP alternatives.

HTAP or mixed workloads

SingleStore fits applications that handle both transactional updates and analytical queries in the same database. This includes real-time inventory systems, fraud detection, personalization engines, and operational analytics.

If your application requires strong consistency, ACID transactions, and the ability to update individual records while running analytics, SingleStore's hybrid storage model provides the necessary capabilities.

No-ops developer experience

Managed services reduce the operational complexity of running ClickHouse^® or SingleStore yourself. Tinybird provides a managed ClickHouse^® platform that handles infrastructure, scaling, and performance tuning while offering developer-focused features like API endpoints and CI/CD integration.

For teams that want to move quickly and focus on building features rather than managing databases, a managed service reduces time to production from weeks to days. Sign up for a free Tinybird plan to try managed ClickHouse^® without infrastructure overhead.

Move fast with managed ClickHouse^® on Tinybird

Tinybird is a managed ClickHouse^® service designed for developers who want to integrate real-time analytics into their applications without worrying about cluster management, scaling, or DevOps.

Start in minutes with the Tinybird CLI

curl -L tinybird.co | sh
tb login
tb create --prompt "Build the backend for a web analytic API"

Resources in Tinybird are defined as code using plaintext files. This approach allows you to version control your analytics infrastructure and test changes locally before deploying.

Deploy API endpoints with CI/CD

Tinybird transforms SQL queries into production-ready REST APIs with built-in authentication, rate limiting, and caching. Define your query in a pipe file, add parameters for filtering, and deploy with a single command:

tb --cloud deploy

This creates a hosted API endpoint that you can call from your application backend, handling the need to manage database connections, query optimization, or API infrastructure.

Sign up for a free Tinybird plan

Tinybird offers a free tier with 10 GB of storage and 1,000 API requests per day, which is enough to build and test real-time analytics features. Sign up for a free account to start using managed ClickHouse^® without credit card or infrastructure setup.

FAQs about ClickHouse^® vs SingleStore

How difficult is migrating from PostgreSQL to ClickHouse^® or SingleStore?

ClickHouse^® requires schema redesign for columnar optimization, which means you'll rethink primary keys, sort orders, and data types, though companies like Skool found queries went from minutes to seconds after migration. SingleStore offers more direct PostgreSQL compatibility with similar SQL syntax and transaction support, making migration easier for transactional workloads.

Can ClickHouse^® or SingleStore guarantee zero data loss during multi-zone failures?

Both databases support replication across availability zones. SingleStore's synchronous replication provides stronger consistency guarantees compared to ClickHouse^®'s asynchronous replication model, which can result in data loss if a node fails before replication completes.

Do both databases support GDPR-compliant data localization requirements?

Both can be deployed in specific geographic regions. ClickHouse^® offers more granular control over data placement through custom sharding policies, which allows you to keep certain data in specific locations based on regulatory requirements.

What are the practical limits on join operation sizes in each database?

ClickHouse^® handles larger analytical joins better due to columnar storage and efficient memory usage. SingleStore optimizes smaller transactional joins through its hybrid row-columnar approach, but very large joins can be slower than ClickHouse^® for pure analytical workloads.

How do backup and disaster recovery processes compare between the two?

ClickHouse^® uses filesystem snapshots and incremental backups, which can be automated using tools like clickhouse-backup. SingleStore provides both logical and physical backup options with point-in-time recovery capabilities, making it easier to restore to a specific moment in time.

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