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The most useful lakehouse layout benchmark is not a race between isolated SQL statements. It is a controlled experiment that runs the scans, filters, joins, updates and refreshes your table actually receives, on the same data scale, engine, runtime and storage configuration. Record both read and write behavior, repeat each run under a declared cache policy, and preserve the diagnostics that explain why one layout wins.
This method shows how to build that experiment, including a practical query matrix, refresh tests, file-layout measurements, reproducibility controls and a way to interpret historical results without treating them as universal rankings.
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What a layout benchmark should answer
A benchmark should answer one bounded question: which layout performs better for these queries, at this scale, on this engine and version? Table format, file organization, reader implementation and query planning can all change the result. A result from one runtime therefore describes that configuration, not a permanent ranking of Delta Lake, Apache Hudi or Apache Iceberg.
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Start by writing the decision you need to make. Examples include whether to add a partition key, whether to compact small files, whether to use a clustering feature, or whether a copy-on-write (CoW) or merge-on-read (MoR) table mode fits an update-heavy workload. The decision determines which measurements matter.
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Describe the production workload before choosing queries
Inventory the table’s consumers and write down the predicates, grouping columns, joins, freshness target and update cadence. A benchmark built around a convenient query can favor a layout that is poor for the rest of the workload.
Separate the main read patterns
- Scan and aggregation: large ranges or most of a fact table, followed by grouping, sorting or aggregation. These expose sequential-read throughput, scan efficiency and shuffle behavior.
- Selective filtering: predicates that should eliminate most partitions or files. Measure files and bytes skipped as well as elapsed time; a fast result can come from pruning, a different reader, or both.
- Joins: joins between fact and dimension tables, especially when one side is filtered first. Keep join keys, table sizes and statistics fixed across variants.
- Point or narrow lookups: requests for a small key or time range. These reveal metadata and small-file overhead that broad scans can hide.
Model writes and freshness
If the table receives corrections, upserts or periodic refreshes, benchmark those operations and then run reads against the changed table. A merge strategy can trade write cost for read cost. Capture initial load time, incremental write or merge time, compaction or optimization time, and post-update query latency.
Use standardized workloads carefully
TPC-DS is useful when its analytical patterns resemble your system, but it is not a substitute for production-shaped queries. Berkeley’s LHBench adapted TPC-DS for lakehouse testing, then added TPC-DS Refresh, a synthetic Merge Microbenchmark and a Large File Count test. That combination exposes update and metadata behavior that a query-only run can miss.
Build a controlled comparison matrix
For every layout variant, keep the factors below constant unless the factor itself is the subject of the experiment. If you change table format and layout at the same time, label the result as a format-and-layout comparison rather than evidence about layout alone.
| Control or disclosure | What to record |
|---|---|
| Data | Generation method, scale, row counts, value distributions and refresh contents |
| Table implementation | Format and exact version; CoW or MoR mode; writer and reader compatibility |
| Layout | Partition or clustering keys, sort settings, file count, file-size distribution and compaction state |
| Engine | Query engine, version, connector and relevant optimizer settings |
| Infrastructure | Cloud and object store, compute shape, worker count, memory, autoscaling and runtime image |
| Execution policy | Cold or warm cache, cache-clearing procedure, concurrency and query order |
| Run protocol | Warm-up policy, repetitions, timeout, failure handling and summary statistic |
Keep raw measurements for every run. Report the median or another declared statistic, and show variance when it affects the decision. LHBench’s 3 TB TPC-DS results ran each query three times and reported medians; that is a concrete protocol to emulate, not a universal minimum.
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Prepare layout variants without hidden advantages
Start from identical logical data
Generate one canonical dataset and use the same row-level contents for every variant. Verify row counts, null rates, key distributions and aggregate checksums before testing. A layout cannot be credited for a difference caused by data generation.
Document physical state
Record file count and size distribution, not just an average. A table with a few very large files and thousands of tiny files can have the same average as a uniformly organized table but a very different planning and scan cost. Capture whether compaction, clustering or optimization has completed and whether statistics were regenerated.
Test partitioning as a hypothesis
Partition keys should match common filters without creating excessive partitions or tiny files. Databricks documentation recommends avoiding partitioning tables below 1 TB and says to expect at least 1 GB per partition in its Delta Lake context. Those are vendor recommendations, not industry-wide thresholds; measure them against your data and access patterns.
Test clustering when access patterns change
Databricks describes liquid clustering as an alternative to fixed partitioning and Z-order for supported tables, with keys that can be changed as needs evolve. Its documentation says liquid clustering is generally available for Delta Lake tables on Databricks Runtime 15.4 LTS and later, while Apache Iceberg support is listed as public preview on Runtime 16.4 LTS and later. Confirm current runtime support, writer behavior and reader compatibility before using those versions in a production test.
The same guidance highlights high-cardinality filters, skew, rapidly growing tables, concurrent writes and partition keys that produce too many or too few partitions as cases to evaluate. Treat that as a candidate hypothesis, then compare a clustered table with the partitioned or unoptimized baseline using your own queries.
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Design the query set
Use a small, representative suite rather than hundreds of unrelated statements. Tag every query by class and business purpose so an aggregate score does not hide a serious regression in one access pattern.
Read suite
- Select at least one scan-heavy aggregation that reads a large fraction of the table.
- Add selective predicates on the columns users filter most often, including a high-cardinality key if it is common in production.
- Include a join representative of the largest or most latency-sensitive relationship.
- Include a narrow lookup or short time-window query to expose metadata and small-file overhead.
- Include any query whose grouping, ordering or window operations create significant shuffle.
Refresh and merge suite
For changing tables, run the reads once before the update and again after it. Include the exact update shape: inserts, updates, deletes, late-arriving records and key collisions. Measure the write operation separately from the first post-update read so you can see whether a faster merge simply moves work into query execution.
Large-file-count stress test
Vary file count while holding logical data and file size targets constant. This isolates metadata and planning sensitivity. LHBench’s historical test used 10 MB files and file counts from 1,000 through 200,000.
Run the benchmark reproducibly
- Provision one fixed environment. Pin engine and runtime versions, compute shape, worker count, object-store region and relevant configuration. Do not resize a cluster between layout variants.
- Load and validate each table. Compare row counts and checksums, then record file counts, size distribution, statistics and optimization state.
- Declare cache behavior. Decide whether each run is cold-cache, warm-cache or a separate pair of measurements. Use the same cache-clearing and warm-up procedure for every variant.
- Warm up the engine consistently. If compilation or session initialization is excluded, run the same warm-up statements and exclude them from every variant.
- Execute in a controlled order. Randomize or rotate query order when possible so object-store, cluster and cache effects do not favor one layout.
- Repeat each query. Use enough repetitions to expose variance; three runs, with a reported median, matches the published LHBench example.
- Capture failures and timeouts. Record them as outcomes with the timeout value and error text. Never omit a failed layout from the denominator.
- Run refreshes and merges. Measure write duration, files created or rewritten and compaction work, then execute the post-update read suite under the same policy.
- Archive run-level data. Keep query text, plans, event logs, metrics and timestamps so another operator can reproduce the summary.
Measure more than elapsed time
Elapsed time is the outcome users feel, but it rarely explains the outcome. Collect the following for every query where the engine exposes them.
- Bytes read and files opened: shows whether pruning reduced physical work.
- Files skipped: distinguishes effective partition, clustering or statistics pruning from a full scan.
- Planning and metadata time: reveals the cost of listing and evaluating a large file set.
- Task and shuffle metrics: identify skew, parallelism limits and downstream stages that dominate runtime.
- Scan throughput: helps separate storage or reader differences from layout effects.
- Write and merge time: include files rewritten, newly generated files and any compaction.
- Post-update latency: shows read amplification after incremental changes.
- Resource use: capture compute utilization and spill where available, because a shorter query that consumes substantially more resources may not be cheaper to operate.
Inspect the physical plan and reader events whenever two layouts differ. The CIDR 2023 comparison found that tested systems often produced the same Spark query plan, with much of the performance difference coming from data-reading time. It also documented cases involving metadata operations and API-driven plan differences. This is why a runtime number alone cannot tell you whether pruning, file size, metadata handling or a different plan caused the result.
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Benchmark CoW and MoR as a read-write decision
Copy-on-Write rewrites files containing changed rows during an update. That increases write amplification but can leave readers with a simpler, consolidated representation. Merge-on-Read keeps some update data separate and defers work to reads, which can reduce merge time while increasing post-merge read work.
Measure both sides with the same update set. A merge that finishes quickly is not a win if every dashboard becomes slower until compaction runs. Conversely, a slower write may be acceptable when readers have strict latency requirements and updates are infrequent.
Use published crossover figures only as test ideas
In LHBench’s synthetic configuration, MoR began to outperform CoW for merge time at 100,000 updated rows, while post-merge latency was higher for MoR. That threshold belongs to that test’s data, versions and infrastructure; it is not a general crossover point.
The same 2022 results reported Hudi MoR merges 1.3× faster than Hudi CoW, with post-merge queries 3.2× slower in the tested refresh configuration. For Iceberg 0.14.0, MoR merges were 1.4× faster than CoW and post-merge query performance remained similar in that test. Re-run the comparison with your update percentage, file sizes, compaction schedule and reader version before selecting a mode.
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Berkeley LHBench’s published 3 TB TPC-DS comparison ran Spark on AWS EMR 6.9.0 with Spark 3.3.0, S3 storage, Delta Lake 2.2.0, Hudi 0.12.0 and Iceberg 1.1.0. It reported query runtime on Delta Lake at 1.4× faster than Hudi and 1.7× faster than Iceberg in that setup. Those are 2022-era measurements with specific readers, file layouts and plans; they should motivate a controlled experiment rather than predict current rankings.
| Published test | Configuration or result | How to use it |
|---|---|---|
| 3 TB TPC-DS reads | Each query ran three times; medians were reported. Delta was 1.4× faster than Hudi and 1.7× faster than Iceberg in the stated environment. | Copy the repetition and reporting pattern, then substitute current versions and representative queries. |
| Refresh benchmark | 100 GB base dataset; 10 refreshes of 3% each; queries Q3, Q9, Q34, Q42 and Q59 before and after merges. | Use the before/after structure when updates matter, while matching your own refresh size. |
| Hudi merge modes | MoR merges were 1.3× faster than CoW; post-merge queries were 3.2× slower in the tested configuration. | Measure write and read cost together; do not generalize the ratio. |
| Iceberg merge modes | Iceberg 0.14.0 MoR merges were 1.4× faster than CoW; post-merge query performance was similar in that test. | Repeat with your reader and compaction behavior. |
| Large file-count test | 10 MB files, from 1,000 to 200,000 files; Delta showed 7×–20× better performance in the 200,000-file case. | Stress metadata overhead at your expected file counts and sizes. |
Interpret results through the execution path
When selective queries improve
Check files skipped, bytes read and planning time. If skipped files increase and bytes fall, the layout likely improved pruning. If runtime falls but bytes remain constant, investigate reader implementation, caching, parallelism or a changed plan before crediting the layout.
When broad scans improve
Inspect file-size distribution, scan throughput and task balance. Larger files can reduce per-file overhead and improve sequential scans, while very large files may reduce parallelism or make narrow searches expensive. Databricks documentation summarizes the trade-off as large files tending to help scans and smaller files helping searches; use that as a hypothesis, not a rule that overrides measurements.
When planning dominates
Compare metadata time and file-listing operations. A layout with many small files can lose before scan tasks begin. The large-file-count stress test is valuable because ordinary query suites may not reach the file counts at which this overhead becomes visible.
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Save the physical plans and identify which operator, reader or metadata API changed. A format comparison can include implementation differences that are not physical-layout effects. State that distinction explicitly in the report.
Common benchmark failures and fixes
- Only one query is tested: add scan, selective, join and narrow-lookup classes so one access pattern does not decide the layout.
- Warm-cache results are presented as general performance: publish separate cold and warm policies, or use one policy consistently and state it.
- Compaction state differs: compact or optimize all variants using the same trigger and record the resulting files.
- Versions are omitted: pin table-format, engine, connector and runtime versions; a reader upgrade can change results without any layout change.
- Write cost is ignored: include merge or refresh duration and post-update reads, especially for MoR.
- Failures disappear from the report: retain timeout and error outcomes and explain whether they block production use.
- Average file size hides outliers: report a distribution or histogram and the total file count.
- A vendor threshold is treated as a standard: label partition and clustering guidance as platform-specific and verify it with your data.
- Results are over-generalized: attach every headline number to its data scale, versions, infrastructure and run protocol.
Report results so another team can reproduce them
Publish a short environment block before the charts. Include dataset scale and generation, table format and version, engine and runtime, cloud and storage, compute shape, layout keys, file counts and sizes, compaction state, cache policy, query order, repetition count, timeout and summary statistic.
For each query, show median runtime alongside bytes read, files opened and skipped, planning time and failures. For refresh tests, show merge time, files rewritten, post-merge latency and whether compaction was included. Preserve run-level values in an appendix or machine-readable file so readers can assess variance rather than seeing only a single score.
End with a decision tied to the workload: for example, choose the layout that meets selective-query latency without exceeding the refresh window, or accept higher merge cost to keep dashboard reads within their service objective. If no variant wins every class, document the trade-off and the operating policy that resolves it.
Quick Recap
A concise checklist
- Workload consumers, filters, joins, freshness and update cadence are written down.
- Scan, selective, join and narrow-lookup queries are represented.
- Refresh or merge tests and post-update reads are included where relevant.
- Data, versions, compute, storage, layout state and cache policy are fixed and disclosed.
- Each query is repeated, with a declared statistic and raw runs retained.
- Bytes, files, planning time, task behavior and write metrics accompany elapsed time.
- Failures and timeouts remain visible.
- Conclusions are limited to the tested scale, versions and infrastructure.
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