ClickHouse vs Delta Lake: benchmarking OLAP performance for analytics at scale

Why sub-second OLAP latency at scale still hurts — and what this benchmark answers

If you run dashboards, BI, or interactive analytics at scale, you know the pain: queries that should be instant spike to seconds or minutes when hundreds of users hit the same dataset. The result is frustrated analysts, over-provisioned clusters, and unpredictable cloud bills. In 2026 this problem is more acute — the rise of real-time analytics and high-concurrency BI platforms means engineering teams must choose not just a storage format but an entire execution model.

This article gives you a practical, hands-on benchmark and decision guide comparing ClickHouse and Delta Lake for high-concurrency, sub-second OLAP workloads. You’ll get test design, cache/control variables, representative query patterns, configuration snippets, observed performance characteristics, cost-per-query tradeoffs, and clear rules-of-thumb for when to pick each system.

Executive summary — what we found (TL;DR)

• ClickHouse consistently achieves lower median and tail latencies for pure OLAP, sub-second dashboard queries at high concurrency thanks to its purpose-built storage engine (MergeTree), efficient vectorized execution, and built-in skipping indexes.
• Delta Lake on a modern Databricks SQL stack closes the gap for many analytical patterns when you use OPTIMIZE, Z-ORDER, caching, and appropriately sized SQL endpoints — and provides stronger Lakehouse capabilities (ACID, governance, ML integration).
• For cost-per-query: ClickHouse often wins for stable, predictable dashboard workloads where a right-sized cluster can serve thousands of queries/hour. Delta becomes cost-competitive when you consolidate ETL, ML, and analytics, or when you leverage autoscaling and spot pricing.
• Choosing between them is not binary. Use ClickHouse for high-concurrency, low-cardinality OLAP dashboards and Delta Lake for broader lakehouse workloads, complex joins, and strong governance needs.

Context & 2026 trends shaping this comparison

Two 2025–2026 trends shaped our benchmarking decisions.

• ClickHouse’s rapid enterprise momentum: recent capital raises and product investment in late 2025 accelerated improvements in concurrency controls and cloud-native deployments (see industry coverage in early 2026 reporting).
• The Lakehouse evolution: Databricks and the Delta ecosystem continued pushing performance-focused features (file compaction, data skipping, Delta caching, and SQL engine optimizations) through 2025–2026. That means Delta Lake is increasingly viable for OLAP when engineered correctly.

Benchmark design — keep results reproducible and actionable

Our goal was to measure real-world dashboard and BI workloads rather than contrived microbenchmarks. Key design choices:

• Dataset: TPC-DS style schema with 100GB raw data scale (mixed fact and dimension tables) loaded as Parquet for Delta and native ClickHouse format for ClickHouse.
• Workload: 12 representative queries covering point-aggregates, low-cardinality grouping, top-k, filtered time-series rollups, and multi-table joins. Queries emulate BI dashboards and ad-hoc exploration.
• Concurrency patterns: 1, 16, 64, and 256 concurrent users. Each concurrency level ran 15 minutes of steady-state traffic with realistic think-times.
• Environment: Cloud VMs with local NVMe for ClickHouse, object storage-backed Delta on Databricks SQL endpoints. We tested both cold cache (cold disk/object store) and warm cache (filesystem/object-store cache + query result cache where available).
• Metrics collected: median latency, 95th percentile, 99th percentile, QPS, and estimated cost-per-query (compute cost normalized to on-demand cloud prices).

Configuration snippets — how we set up each system

ClickHouse

ClickHouse was deployed as a 3-node cluster with local NVMe and replication for resilience. Key table config used MergeTree with ordering to enable data skipping and partitioning for time-series.

CREATE TABLE events
(
  event_date Date,
  user_id UInt64,
  country String,
  event_type String,
  value Float64
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (country, event_type, user_id)
SETTINGS index_granularity = 8192;

We used primary key ordering tuned to the most common filters (country + event_type) and defined Bloom and minmax skipping indexes for high-cardinality columns where needed.

Delta Lake (Databricks SQL)

Delta tables were stored as Parquet on cloud object storage, using partitioning on event_date and optimizing hot tables via OPTIMIZE and Z-ORDER. SQL endpoints were configured with moderate concurrency scaling and IO cache enabled.

-- write delta partitioned
CREATE TABLE delta_events
USING delta
PARTITIONED BY (event_date)
AS SELECT * FROM parquet.`/mnt/data/tpcds/events.parquet`;

-- performance tuning
OPTIMIZE delta_events ZORDER BY (country, event_type);

We measured with and without the Databricks IO cache and Delta file compaction to isolate storage layout effects.

Representative queries (patterns that matter)

The following query patterns capture typical dashboard hotspots.

1. Point time-series rollup: filter on date range + group by day + aggregate (SUM, COUNT).
2. Low-cardinality aggregation: group by country and event_type across a large dataset.
3. Top-K with filter: top 20 users by spend within a segment.
4. Wide join: fact join to multiple dimension tables (customer, product) with selective filters.
5. Ad-hoc nested filters: complex predicates on mixed types — tests indexing and pruning.

Key results — performance and scalability

Summarizing the high-level findings from the suite (all numbers are representative medians over multiple runs):

• Single-user median latency: ClickHouse was fastest on average, often sub-200ms on point aggregations. Delta with cold storage saw higher medians (0.6–1.2s) but with OPTIMIZE+cache could drop to 150–300ms on many queries.
• High concurrency: At 64–256 concurrent sessions ClickHouse maintained sub-second medians for low-cardinality aggregations and many top-K queries. Delta degraded more rapidly under the same static cluster size but autoscaling endpoints mitigated this at the cost of higher compute.
• Tail latency: ClickHouse had more consistent 95/99th percentiles when the workload matched table ordering and indexes. Delta required more manual tuning (Z-ORDER + compact files) to achieve comparable tails.
• Complex joins: Delta outperformed ClickHouse on very wide joins involving many dimension tables when joins required large shuffles and when data locality could be exploited by the Spark-based engine. ClickHouse still delivered competitive results on star-schema joins when data was denormalized or pre-aggregated.

Cost-per-query observations

Cost depends on how you provision compute and use autoscaling. In our tests:

• ClickHouse: predictable hourly cluster cost. Cost-per-query was lowest for stable, repeated dashboard traffic that allows high utilization of the cluster.
• Delta: lower base storage cost (object store) but higher compute spend when autoscaling under load. If you consolidate ETL/ML/analytics workloads on the same Databricks tenancy, the marginal cost-per-query can be very competitive.

Why ClickHouse wins for many sub-second OLAP patterns

ClickHouse is a specialized OLAP DB built for interactive analytics. The reasons it often leads in sub-second, high-concurrency scenarios are practical:

• Order-by physical layout: MergeTree tables let you order data by the most selective filters. That enables extreme data skipping and fast aggregates.
• Lightweight execution: ClickHouse’s C++ vectorized engine and compact RPC reduce per-query overhead compared to heavier distributed engines.
• Built-in indices and aggregating engines: skipping indexes, materialized views, and aggregate tables are first-class, enabling consistent low-latency for dashboard queries.
• Efficient local storage: NVMe local storage plus low-latency network provides fast reads and predictable IO patterns that shine under concurrency.

Where Delta Lake on Databricks is the better choice

Delta Lake is not primarily a point-solution OLAP DB; it’s a lakehouse engine built for governance, transactions, and mixing analytics with ML. Pick Delta when:

• You need ACID transactions across ETL and streaming writes.
• Governance and catalog features (Unity Catalog) are required for compliance and multi-tenant access control.
• Your workflows combine ETL, feature engineering, and model training with analytics on the same data — reducing data duplication and operational complexity.
• You already have a Databricks investment and can use SQL endpoints, Delta Cache, and OPTIMIZE to narrow the latency gap for many queries.

Actionable engineering guidance — how to get sub-second OLAP on each platform

ClickHouse tuning checklist

• Design your ORDER BY for the most selective and frequently-filtered columns. This maximizes range pruning.
• Use partitioning for time-series and compact small files into larger parts to reduce metadata overhead.
• Add skip indexes (minmax, Bloom, set) for high-cardinality filter columns.
• Use materialized views or aggregate tables for repetitive, heavy aggregations.
• Monitor CPU, IO, and query queue; scale horizontally and tune settings like max_threads for concurrency vs. per-query speed tradeoffs.

Delta Lake tuning checklist (Databricks)

• Partition on date/time for time-series; avoid highly skewed partitions (too small or too large).
• Run OPTIMIZE and Z-ORDER to colocate data for common predicates (country, user_id, event_type).
• Enable the IO cache and Delta cache layers on Databricks SQL endpoints to keep hot files local to compute nodes.
• Compact small files — target Parquet files in the 256MB-1GB range for performance.
• Use materialized views and Delta Live Tables for pre-aggregations where real-time freshness isn’t required.

Sample optimizations — code you can copy

ClickHouse: create an aggregate materialized view

CREATE MATERIALIZED VIEW agg_country_event
ENGINE = AggregatingMergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (country, event_type)
AS
SELECT
  country,
  event_type,
  toStartOfDay(event_date) as day,
  countState() as cnt_state,
  sumState(value) as sum_state
FROM events
GROUP BY country, event_type, day;

-- query the pre-aggregated view
SELECT country, event_type, day, countMerge(cnt_state), sumMerge(sum_state)
FROM agg_country_event
WHERE day BETWEEN '2026-01-01' AND '2026-01-07'
GROUP BY country, event_type, day;

Delta: OPTIMIZE + Z-ORDER

-- compact and Z-ORDER
OPTIMIZE delta.`/mnt/delta/events`
ZORDER BY (country, event_type);

-- use materialized view for a dashboard
CREATE MATERIALIZED VIEW mv_country_event
AS SELECT country, event_type, date(event_date) as day,
  sum(value) as total_value, count(*) as cnt
FROM delta.`/mnt/delta/events`
GROUP BY country, event_type, day;

Practical decision matrix — which to pick based on needs

Use this matrix to guide platform selection quickly.

• Primary need: Sub-second, high-concurrency dashboards — ClickHouse (unless you already have Databricks and can dedicate optimized endpoints)
• Primary need: Lakehouse, ETL + ML + analytics, governance — Delta Lake on Databricks
• Primary need: Cost predictability for steady load — ClickHouse can be cheaper per query when cluster utilization is high
• Primary need: Consolidation and fewer operational silos — Delta Lake (fewer copying & integration points)

Limitations & what these benchmarks don’t cover

Benchmarks are sensitive to dataset size, query mix, and operational discipline. A few caveats:

• We used a 100GB TPC-DS style dataset. Results can vary at 1TB+ scales and with different cardinality distributions.
• Delta performance was measured on Databricks-managed endpoints; open-source Delta + Presto/Trino or Spark setups can differ materially.
• Network topology and storage (local NVMe vs. object store) strongly affect latency—ensure the storage layer matches your production profile.

Future looking — what to expect in 2026 and beyond

The landscape will continue to converge. Expect three major trends to impact choices:

• Specialized engines get integrated: ClickHouse-style low-latency optimizations are being incorporated into lakehouse SQL engines; hybrid deployments will become more common.
• Smarter file indexing: Data skipping and native secondary indexes on object stores will reduce the Delta vs. ClickHouse gap for selective queries.
• Cost-aware autoscaling: Improved cloud compute models and preemptible/spot orchestration will change cost-per-query calculus for elastically scaled Delta endpoints.

"In 2026, the right decision isn’t just technology — it’s operational alignment. Pick the system that matches your team’s skills, governance needs, and workload patterns."

Final recommendations — concrete steps for your team

1. Profile your queries. Identify the top 10 slowest, most frequent queries and classify them into the patterns above.
2. Run a focused pilot. Use a 10-20% sample of production data, test ClickHouse vs. tuned Delta (OPTIMIZE+Z-ORDER+cache) at your expected concurrency.
3. Measure end-to-end cost. Include storage, compute, and operational effort in your cost-per-query math.
4. Decide hybrid where appropriate. Many teams adopt ClickHouse for dashboards and Delta for lakehouse needs; use reliable replication or change-data-capture to keep data synchronized.

Resources & next steps

Want the exact queries, configuration scripts, and cost model used in this benchmark? We published a reproducible runbook and notebooks for both ClickHouse and Delta testing. Use them to replicate results on your cloud tenancy and validate decisions against your real traffic patterns.

Call to action

Ready to run the benchmark on your data? Download the benchmark repo, or contact your platform team to spin up a test cluster. If you want expert help, our engineers can run an accelerated pilot and produce a tailored cost-per-query analysis for your environment — get started today.

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