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The fastest lakehouse layout is the one that serves your workload on your engine, data scale and maintenance schedule—not a universal winner. A useful benchmark keeps software, hardware, data and cache conditions constant; runs scan, filter, join and update workloads that resemble production; and records both latency and the execution details that explain it.
What a representative layout benchmark should answer
A benchmark should let you answer a narrowly defined question: which table layout performs better for these queries, at this data volume, on this engine and version, with this update pattern? That framing prevents a common mistake—treating a result from one table format, file size or cloud configuration as a permanent ranking.
Separate three things in your experiment:
- Table format: the transaction-log and storage format, such as Delta Lake, Apache Hudi or Apache Iceberg.
- Physical layout: partition keys, clustering or data-skipping settings, file-size distribution, compaction state and the number of files.
- Execution environment: query engine and version, compute shape, object storage, runtime settings, reader implementation and cache state.
If you change all three at once, you can measure an end-to-end product choice, but you cannot claim that layout alone caused the difference. State which question your test addresses before collecting timings.
Start with the production workload
Inventory the access patterns
Write down who reads the table, which columns they filter or group by, which joins they perform, how fresh the data must be and how often rows are inserted, updated or deleted. Record high-cardinality predicates, skewed values, common date ranges, tenant or customer filters, and queries that repeatedly scan recent partitions. Include scheduled reports, dashboards, ad hoc analysis and data-science jobs if they share the table.
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Turn that inventory into query templates. Keep a copy of each production query’s logical shape, but parameterize dates, keys and limits so that every layout receives the same inputs. Preserve realistic selectivity: a query returning one tenant is not interchangeable with one returning half the table.
Use separate classes for separate behaviors
- Scan and aggregation: full or broad date-range scans with grouping, sorting and aggregates. These expose throughput and the cost of reading many files.
- Selective filters: predicates expected to skip most files. Measure whether partitioning, clustering or data skipping actually reduces bytes and files read.
- Joins: joins using the keys and table sizes seen in production. Capture shuffle and spill behavior as well as elapsed time.
- Point or narrow lookups: single-key or small-range requests when interactive latency matters.
- Refresh and update operations: incremental loads, merges, deletes, compaction and optimization. A layout that wins reads may impose unacceptable write amplification.
Do not average these classes into one unexplained score. Report each class separately, then explain which class drives the business decision.
Choose a standard workload without losing production realism
TPC-DS is useful when its schemas and query shapes resemble your analytical workload. It supplies a repeatable scale factor and known queries, making results easier to compare across teams. It is not a substitute for production-specific filters, joins or update behavior.
The Berkeley LHBench work used TPC-DS queries and then added lakehouse-specific tests: TPC-DS Refresh, a synthetic Merge Microbenchmark and a Large File Count test. That combination is a good model because query-only runs can miss merge costs, post-update read amplification and metadata overhead.
A practical suite has two layers:
- Reference layer: a stable subset of TPC-DS or another public workload for repeatability.
- Workload layer: parameterized production queries and refresh jobs selected from your access-pattern inventory.
Publish the exact query identifiers, SQL text or templates, parameter values, scale factor and data-generation procedure. If a query is excluded because your engine cannot run it, record the failure rather than silently dropping it.
Define layout variants before loading data
Make each candidate explicit. For every variant, record the format, partition or clustering keys, target file size, compaction and optimization state, sorting or data-skipping settings, write mode and any table properties. Load identical logical rows into every variant using the same generation and ingestion order.
| Dimension | What to hold constant | What may vary as the test factor |
|---|---|---|
| Data | Schema, row values, scale, null distribution, skew and generation seed | Only the layout or format under test |
| Compute | Worker count, instance type, memory, autoscaling limits and runtime configuration | A separate experiment comparing compute profiles |
| Software | Engine version, connector or reader version, table-format library and runtime image | A separate version-upgrade experiment |
| Storage | Object store, region, network path and encryption settings | A separate storage-placement experiment |
| State | Compaction policy, statistics, metadata freshness and table history | Explicit cold, warm or post-maintenance scenarios |
| Queries | SQL text, parameters, concurrency and order | The query class or parameter set being studied |
When comparing table formats and layouts together, label the result accordingly. “Format A was faster than format B in this environment” is supportable; “partitioning caused the improvement” is not if the reader implementation and file-writing behavior also changed.
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Control data scale, file sizes and file counts
Run at the scale where your decision matters. A layout that looks excellent at 100 GB may behave differently at multiple terabytes because metadata, listing and planning costs grow with file count. Preserve the file-size distribution produced by your intended ingestion process, then create a separate controlled test if you want to isolate file size.
Record, for every table:
- Total logical and physical bytes.
- Number of data files and their size distribution (minimum, median, maximum and meaningful percentiles).
- Number of partitions or clustering ranges.
- Statistics or data-skipping metadata available to the reader.
- Delete files, change files or log records created by updates.
- Compaction and optimization history.
Small files can increase listing, planning and task-launch overhead; very large files can reduce parallelism and make selective searches read more data than necessary. Treat the balance as a hypothesis and measure it rather than adopting a universal target.
Use a repeatable run protocol
Elapsed time from a single run is not a benchmark. Define the protocol before execution and apply it identically to every variant.
- Prepare an isolated table: load the same dataset, verify row counts and checksums, and document the resulting file inventory.
- Set the cache policy: choose cold-cache, warm-cache or both. For cold runs, clear or isolate filesystem, object-store and engine caches where your platform permits; for warm runs, execute a stated warm-up sequence.
- Stabilize the environment: pin the engine and runtime versions, worker configuration, autoscaling limits, object-store region and concurrency. Note other workloads that could contend for resources.
- Run a warm-up: compile or initialize the reader without including one-time startup costs in the reported query metric, or include those costs consistently in every variant.
- Execute each query repeatedly: use the same order and parameters for every table. Three runs, with the median reported, is the protocol used for LHBench’s 3 TB TPC-DS results; it is an example, not a universal minimum.
- Capture every outcome: retain successful timings, errors, retries, executor loss, out-of-memory events and timeouts. A failure is a result and must not be omitted from the denominator.
- Run maintenance scenarios: measure initial load, incremental write or merge, compaction and the first reads after each operation when those operations occur in production.
- Preserve raw records: store run-level metrics, query plans, configuration snapshots and timestamps so another engineer can calculate a different summary statistic.
Report the median and show spread—minimum/maximum or percentile values—rather than presenting only a single rounded number. If concurrency matters, add a separate concurrent test; do not mix single-user and multi-user timings.
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Collect diagnostics that explain the timing
Latency tells you that variants differ; execution diagnostics tell you why. Collect the following whenever the engine exposes them:
- Bytes read and bytes returned.
- Files opened, files skipped and file-level or partition-level pruning.
- Planning, metadata and log-replay time.
- Number of tasks, task-duration distribution, shuffle bytes, spill and skew.
- Scan throughput, CPU time and input wait time.
- Write, merge, compaction and commit time.
- Delete-file or change-file processing after updates.
Compare physical query plans as well as SQL text. The CIDR 2023 comparison reported that systems often produced the same Spark query plan, while most of the observed difference came from data-reading time. It also documented cases in which metadata operations and API-driven plan differences mattered. If plans match but bytes or files read differ, investigate the reader and file layout; if plans differ, do not attribute the result to layout alone.
Measure reads and writes together
Copy-on-Write
Copy-on-Write (CoW) rewrites files containing changed rows during an update. This can increase write amplification and commit work, but readers generally see consolidated data without needing to merge separate update files.
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Merge-on-Read
Merge-on-Read (MoR) writes change data separately and defers some reconciliation to readers. Writes can be cheaper, while post-update reads may pay extra merge and metadata costs. Measure both sides of that trade-off:
- Time the merge or refresh itself.
- Measure the first query after the merge and steady-state queries afterward.
- Record files and bytes read, including change or delete files.
- Measure compaction required to restore read performance.
In LHBench’s historical synthetic test, MoR began outperforming CoW for merge time at 100,000 updated rows, while post-merge latency was higher for MoR. That threshold belongs to the tested configuration, not a general crossover point.
Use refresh tests that resemble table maintenance
LHBench’s refresh design started with a 100 GB base dataset, applied 10 refreshes of 3% each and ran five queries—Q3, Q9, Q34, Q42 and Q59—before and after merges. You can adapt the pattern to your cadence: choose a base snapshot, apply the same number and size of increments your pipeline produces, and run representative reads after each update.
Keep separate columns for:
- Incremental file-writing time.
- Transaction or commit time.
- Merge time by operation type.
- Post-update query latency.
- Compaction or optimization time.
- Storage growth and residual change-file count.
Do not report only the final query speed. A layout that requires frequent expensive maintenance may cost more operationally even if its compacted snapshot reads quickly.
Stress metadata with a large-file-count test
To expose planning and listing behavior, vary file count while keeping logical data and file size controlled. LHBench’s large-file-count test used 10 MB files and increased the count from 1,000 to 200,000. In that setup, Delta showed 7×–20× better performance than the alternatives in the 200,000-file case. Those figures describe that historical setup; they are not a current universal ranking.
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- Time spent listing or discovering files.
- Metadata-log replay and planning latency.
- Driver memory and task-generation time.
- Number of files actually opened after pruning.
- Failure or timeout behavior at the largest file counts.
Run this as a distinct stress test. It answers whether an operationally plausible small-file condition overwhelms the reader; it does not predict ordinary performance when tables are compacted.
Published evidence and how to interpret it
| Result | Conditions and qualification |
|---|---|
| Delta queries were 1.4× faster than Hudi and 1.7× faster than Iceberg | Berkeley LHBench/CIDR-era 3 TB TPC-DS results using Spark on AWS EMR 6.9.0 (Spark 3.3.0), S3, Delta Lake 2.2.0, Hudi 0.12.0 and Iceberg 1.1.0; medians of three runs. Historical evidence, not a present-day ranking. |
| Hudi MoR merges were 1.3× faster than Hudi CoW | LHBench refresh configuration; post-merge queries were 3.2× slower for MoR. The trade-off is specific to that test. |
| Iceberg 0.14.0 MoR merges were 1.4× faster than CoW | LHBench test in which post-merge query performance remained similar between modes. |
| Delta was 7×–20× faster in the 200,000-file case | Large File Count test with 10 MB files and file counts from 1,000 through 200,000; do not generalize beyond the tested setup. |
These results are valuable because they show why a benchmark must include reader behavior, file inventory and maintenance—not because they establish a permanent winner. Software versions, writers, readers, file sizes and query plans can all change the outcome.
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Test partitioning and clustering as hypotheses
Partitioning can make selective predicates inexpensive when the key aligns with common filters, but too many partitions create small files and metadata overhead. A broad scan may pay for many partition boundaries without gaining useful pruning. Test both the common selective query and the broad scan before choosing a key.
Databricks documentation advises avoiding over-partitioning in its Delta Lake context, recommends no partitioning for tables below 1 TB and says to expect at least 1 GB per partition. These are vendor recommendations for that platform, not industry-wide standards. Measure your own partition sizes, file counts and query behavior.
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Current note on liquid clustering
Databricks describes liquid clustering as a way to simplify data-layout decisions and optimize query performance. Its documentation lists general availability for Delta Lake tables on Databricks Runtime 15.4 LTS and later, and public preview for Apache Iceberg tables on Runtime 16.4 LTS and later. Verify current documentation, writer support and reader compatibility before implementing because feature status and constraints can change.
The guidance particularly targets high-cardinality filters, skew, rapidly growing tables, concurrent writes, changing access patterns and cases where a partition key would create too many or too few partitions. Treat each recommendation as a candidate hypothesis: benchmark the proposed keys, compare maintenance work, and retain a fixed-partition or unclustered control.
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After collecting raw runs, create one row per query and layout. Include these columns:
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|---|---|---|
| Read performance | Median and spread, bytes read, files opened/skipped | Does the layout improve the queries that matter? |
| Planning | Metadata and planning time, driver work, failures | Does file or log growth create an operational ceiling? |
| Write performance | Initial load, incremental write, merge and commit time | Can the pipeline meet its freshness window? |
| Post-update behavior | Immediate and steady-state read latency, change-file count | Does cheaper writing create read amplification? |
| Maintenance | Compaction/reclustering time, storage growth and scheduling frequency | What recurring cost is required to preserve performance? |
| Compatibility | Engine, format, runtime and reader support | Will all required jobs read and write the layout? |
Set acceptance thresholds before looking at the winner—for example, a maximum dashboard latency, a refresh completion window or a cap on maintenance hours. A small read-time gain may not justify a large increase in refresh cost, and a layout that wins medians but times out under realistic file counts may be unsuitable.
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Common benchmark failures and fixes
Using one convenient query
Symptom: a layout wins a single aggregation and is declared best. Fix: add selective filters, joins, point lookups and the actual refresh path; report results by query class.
Changing engine settings between variants
Symptom: each table is tested with its vendor’s preferred runtime or tuning. Fix: hold compute, runtime and reader settings constant for a fair layout comparison, then run a separate optimized-product comparison if desired.
Mixing cold and warm cache runs
Symptom: the first variant benefits from a warm object-store or metadata cache. Fix: isolate cold runs, use a stated warm-up, and never compare an unlabelled mixture.
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Ignoring small files
Symptom: a test uses freshly written files unlike production or assumes compaction is free. Fix: preserve production file-size distribution and measure compaction as part of the workload.
Dropping failures
Symptom: only successful timings appear in the report. Fix: count errors, retries, timeouts and out-of-memory events as outcomes and explain their impact.
Confusing format with layout
Symptom: a difference is attributed to partitioning even though the table format and reader changed. Fix: label the experiment as an end-to-end format comparison or add a controlled same-format layout comparison.
Reporting averages without raw variance
Symptom: a small apparent gain cannot be reproduced. Fix: retain run-level data, report the chosen statistic and show spread, timestamps and resource contention.
What a reproducible report should contain
Publish a methods block beside every result set. Include:
- Dataset source, scale, schema, generation seed and refresh schedule.
- Table format and exact version, engine and exact version, runtime image and reader libraries.
- Cloud, region, object storage, worker types, worker count, memory and autoscaling limits.
- Partition or clustering keys, file-size target, file count and size distribution.
- Compaction, statistics and optimization state before each run.
- Query text or immutable identifiers, parameter values, order and concurrency.
- Cache policy, warm-up procedure, repetition count and summary statistic.
- Raw timings, failures, bytes, files, planning time, shuffle metrics and write metrics.
- Any deviations, retries, throttling or infrastructure incidents.
Attach query plans and table-inventory snapshots where possible. A reader should be able to tell whether a result came from pruning, faster scans, less metadata work, a different plan or simply a warmer cache.
Bottom line for choosing a layout
Build the benchmark around the workload you must serve, not around a format you hope will win. Use representative scans, selective predicates, joins and updates; keep data, software, compute and cache conditions fixed; repeat runs and report medians with variance; and collect files, bytes, planning and maintenance metrics alongside latency. Treat published multipliers and vendor thresholds as context, then validate the candidate layout on your own engine, versions, scale and refresh cadence.
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