LakeBench

🌊 LakeBench is the first Python-based, multi-modal benchmarking framework designed to evaluate performance across multiple lakehouse compute engines and ELT scenarios. Supporting a variety of engines and both industry-standard and novel benchmarks, LakeBench enables comprehensive, apples-to-apples comparisons in a single, extensible Python library.

🚀 The Mission of LakeBench

LakeBench exists to bring clarity, trust, accessibility, and relevance to engine benchmarking by focusing on four core pillars:

1. End-to-End ELT Workflows Matter

   Most benchmarks focus solely on analytic queries. But in practice, data engineers manage full data pipelines — loading data, transforming it (in batch, incrementally, or even streaming), maintaining tables, and then querying.

   LakeBench proposes that the entire end-to-end data lifecycle managed by data engineers is relevant, not just queries.

2. Variety in Benchmarks Is Essential

   Real-world pipelines deal with with different data shapes, sizes, and patterns. One-size-fits-all benchmarks miss this nuance.

   LakeBench covers a variety of benchmarks that represent diverse workloads — from bulk loads to incremental merges to maintenance jobs to ad-hoc queries — providing a richer picture of engine behavior under different conditions.

3. Consistency Enables Trustworthy Comparisons

   Somehow, every engine claims to be the fastest at the same benchmark, at the same time. Without a standardized framework, with support for many engines, comparisons are hard to trust and even more difficult to reproduce.

   LakeBench ensures consistent methodology across engines, reducing the likelihood of implementation bias and enabling repeatable, trustworthy results. Engine subject matter experts are encouraged to submit PRs to tune code as needed so that their preferred engine is best represented.

4. Accessibility starts with pip install

   Most benchmarking toolkits are highly inaccessible to the beginner data engineer, requiring the user to build the package or installation via a JAR, absent of Python bindings.

   LakeBench is intentionally built as a Python-native library, installable via pip from PyPi, so it's easy for any engineer to get started—no JVM or compilation required. It's so lightweight and approachable, you could even use it just for generating high-quality sample data.

✅ Why LakeBench?

• Multi-Engine: Benchmark Spark, DuckDB, Polars, and many more planned, side-by-side
• Lifecycle Coverage: Ingest, transform, maintain, and query—just like real workloads
• Diverse Workloads: Test performance across varied data shapes and operations
• Consistent Execution: One framework, many engines
• Extensible by Design: Add engines or additional benchmarks with minimal friction
• Dataset Generation: Out-of-the box dataset generation for all benchmarks
• Rich Logs: Automatically logged engine version, compute size, duration, estimated execution cost, etc.

LakeBench empowers data teams to make informed engine decisions based on real workloads, not just marketing claims.

💪 Benchmarks

LakeBench currently supports four benchmarks with more to come:

• ELTBench: An benchmark with various modes (light, full) that simulates typicaly ELT workloads:
  • Raw data load (Parquet → Delta)
  • Fact table generation
  • Incremental merge processing
  • Table maintenance (e.g. OPTIMIZE/VACUUM)
  • Ad-hoc analytical queries
• TPC-DS: An industry-standard benchmark for complex analytical queries, featuring 24 source tables and 99 queries. Designed to simulate decision support systems and analytics workloads.
• TPC-H: Focuses on ad-hoc decision support with 8 tables and 22 queries, evaluating performance on business-oriented analytical workloads.
• ClickBench: A benchmark that simulates ad-hoc analytical and real-time queries on clickstream, traffic analysis, web analytics, machine-generated data, structured logs, and events data. The load phase (single flat table) is followed by 43 queries.

Planned

• TPC-DI: An industry-standard benchmark for data integration workloads, evaluating end-to-end ETL/ELT performance across heterogeneous sources—including data ingestion, transformation, and loading processes.

⚙️ Engine Support Matrix

LakeBench supports multiple lakehouse compute engines. Each benchmark scenario declares which engines it supports via <BenchmarkClassName>.BENCHMARK_IMPL_REGISTRY.

Engine	ELTBench	TPC-DS	TPC-H	ClickBench
Spark (Fabric)	✅	✅	✅	✅
DuckDB	✅	✅	✅	✅
Polars	✅	⚠️	⚠️	🔜
Daft	✅	⚠️	⚠️	🔜
Sail	✅	⚠️	✅	✅

Legend:
✅ = Supported
⚠️ = Some queries fail due to syntax issues (i.e. Polars doesn't support SQL non-equi joins, Daft is missing a lot of standard SQL contructs, i.e. DATE_ADD, CROSS JOIN, Subqueries, non-equi joins, CASE with operand, etc.). 🔜 = Coming Soon
(Blank) = Not currently supported

🔌 Extensibility by Design

LakeBench is designed to be extensible, both for additional engines and benchmarks.

• You can register new engines without modifying core benchmark logic.
• You can add new benchmarks that reuse existing engines and shared engine methods.
• LakeBench extension libraries can be created to extend core LakeBench capabilities with additional custom benchmarks and engines (i.e. MyCustomSynapseSpark(Spark), MyOrgsELT(BaseBenchmark)).

New engines can be added via subclassing an existing engine class. Existing benchmarks can then register support for additional engines via the below:

from lakebench.benchmarks import TPCDS
TPCDS.register_engine(MyNewEngine, None)

register_engine is a class method to update <BenchmarkClassName>.BENCHMARK_IMPL_REGISTRY. It requires two inputs, the engine class that is being registered and the engine specific benchmark implementation class if required (otherwise specifying None will leverage methods in the generic engine class).

This architecture encourages experimentation, benchmarking innovation, and easy adaptation.

Example:

from lakebench.engines import BaseEngine

class MyCustomEngine(BaseEngine):
    ...

from lakebench.benchmarks.elt_bench import ELTBench
# registering the engine is only required if you aren't subclassing an existing registered engine
ELTBench.register_engine(MyCustomEngine, None)

benchmark = ELTBench(engine=MyCustomEngine(...))
benchmark.run()

---

Using LakeBench

📦 Installation

Install from PyPi:

pip install lakebench[duckdb,polars,daft,tpcds_datagen,tpch_datagen,sparkmeasure]

Note: in this initial beta version, all engines have only been tested inside Microsoft Fabric Python and Spark Notebooks.

Example Usage

To run any LakeBench benchmark, first do a one time generation of the data required for the benchmark and scale of interest. LakeBench provides datagen classes to quickly generate parquet datasets required by the benchmarks.

Data Generation

Data generation is provided via the DuckDB TPC-DS and TPC-H extensions. The LakeBench wrapper around DuckDB adds support for writing out parquet files with a provided row-group target file size as normally the files generated by DuckDB are atypically small (i.e. 10MB) and are most suitable for ultra-small scale scenarios. LakeBench defaults to target 128MB row groups but can be configured via the target_row_group_size_mb parameter of both TPC-H and TPC-DS DataGenerator classes.

Generating scale factor 1 data takes about 1 minute on a 2vCore VM.

TPC-H Data Generation

from lakebench.datagen import TPCHDataGenerator

datagen = TPCHDataGenerator(
    scale_factor=1,
    target_mount_folder_path='/lakehouse/default/Files/tpch_sf1'
)
datagen.run()

TPC-DS Data Generation

from lakebench.datagen import TPCDSDataGenerator

datagen = TPCDSDataGenerator(
    scale_factor=1,
    target_mount_folder_path='/lakehouse/default/Files/tpcds_sf1'
)
datagen.run()

Notes:

• TPC-H data can be generated up to SF100 however I hit OOM issues when targeting generating SF1000 on a 64-vCore machine.
• TPC-DS data up to SF1000 can be generated on a 32-vCore machine.
• TPC-H and TPC-DS datasets up to SF10 will complete in minutes on a 2-vCore machine.
• The ClickBench dataset (only 1 size) should download with partitioned files in ~ 1 minute and ~ 6 minutes as a single file.

Is BYO Data Supported?

If you want to use you own TPC-DS, TPC-H, or ClickBench parquet datasets, that is fine and encouraged as long as they are to specification. The Databricks spark-sql-perf repo which is commonly used to produce TPC-DS and TPC-H datasets for benchmarking Spark has two critical schema bugs (typos?) in their implementation. Rather than supporting the perpetuation of these typos, LakeBench sticks to the schema defined in the specs. An issue was raised for tracking if this gets fixed. These datasets need to be fixed before running LakeBench with any data generated from spark-sql-perf:

1. The c_last_review_date_sk column in the TPC-DS customer table was named c_last_review_date (the _sk is missing) and it is generated as a string whereas the TPC-DS spec says this column is a Identity type which would map to a integer. The data value is still a surrogate key but the schema doesn't exactly match the specification. Fix via:

   df = spark.read.parquet(f".../customer/")
   df = df.withColumn('c_last_review_date_sk', sf.col('c_last_review_date').cast('int')).drop('c_last_review_date')
   df.write.mode('overwrite').parquet(f".../customer/")

2. The s_tax_percentage column in the TPC-DS store table was named with a typo: s_tax_precentage (is "precentage" the precursor of a "percentage"??). Fix via:

   df = spark.read.parquet(f"..../store/")
   df = df.withColumnRenamed('s_tax_precentage', 's_tax_percentage')
   df.write.mode('overwrite').parquet(f"..../store/")

Fabric Spark

from lakebench.engines import FabricSpark
from lakebench.benchmarks import ELTBench

engine = FabricSpark(
    lakehouse_workspace_name="workspace",
    lakehouse_name="lakehouse",
    lakehouse_schema_name="schema",
    spark_measure_telemetry=True
)

benchmark = ELTBench(
    engine=engine,
    scenario_name="sf10",
    mode="light",
    tpcds_parquet_abfss_path="abfss://...",
    save_results=True,
    result_abfss_path="abfss://..."
)

benchmark.run()

Note: The spark_measure_telemetry flag can be enabled to capture stage metrics in the results. The sparkmeasure install option must be used when spark_measure_telemetry is enabled (%pip install lakebench[sparkmeasure]). Additionally, the Spark-Measure JAR must be installed from Maven: https://mvnrepository.com/artifact/ch.cern.sparkmeasure/spark-measure_2.13/0.24

Polars

from lakebench.engines import Polars
from lakebench.benchmarks import ELTBench

engine = Polars( 
    delta_abfss_schema_path = 'abfss://...'
)

benchmark = ELTBench(
    engine=engine,
    scenario_name="sf10",
    mode="light",
    tpcds_parquet_abfss_path="abfss://...",
    save_results=True,
    result_abfss_path="abfss://..."
)

benchmark.run()

---

Managing Queries Over Various Dialects

LakeBench supports multiple engines that each leverage different SQL dialects and capabilities. To handle this diversity while maintaining consistency, LakeBench employs a hierarchical query resolution strategy that balances automated transpilation with engine-specific customization.

Query Resolution Strategy

LakeBench uses a three-tier fallback approach for each query:

1. Engine-Specific Override (if exists - rare)

   • Custom queries tailored for specific engine limitations or optimizations
   • Example: src/lakebench/benchmarks/tpch/resources/queries/daft/q14.sql -> Daft is generally sensitive to multiplying decimals and thus requires casing to DOUBLE or managing specific decimal types.

2. Parent Engine Class Override (if exists - rare)

   • Shared customizations for engine families, i.e. Spark (not yet leveraged by any engine and benchmark combinations).
   • Example: src/lakebench/benchmarks/tpch/resources/queries/spark/q14.sql

3. Canonical + Transpilation (fallback - common)

   • SparkSQL canonical queries are automatically transpiled via SQLGlot. Each engine registers its SQLGLOT_DIALECT constant, enabling automatic transpilation when custom queries aren't needed.
   • Example: src/lakebench/benchmarks/tpch/resources/queries/canonical/q14.sql

In all cases, tables are automatically qualified with the catalog and schema if applicable to the engine class.

Why This Approach?

Real-World Engine Limitations: Engines like Daft lack support for DATE_ADD, CROSS JOIN, subqueries, and non-equi joins. Polars doesn't support non-equi joins. Rather than restricting all queries to the lowest common denominator, LakeBench allows targeted workarounds.

Automated Transpilation Where Possible: For most queries, SQLGlot can successfully transpile SparkSQL to engine-specific dialects (DuckDB, Postgres, SQLServer, etc.), eliminating manual maintenance overhead and a proliferation of query variants.

Expert Optimization: Engine specific subject matter experts can contribute PRs with optimized query variants that reasonably follow the specification of the benchmark author (i.e. TPC).

Viewing Generated Queries

To inspect the final query that will be executed for any engine:

benchmark = TPCH(engine=MyEngine(...))
query_str = benchmark._return_query_definition('q14')
print(query_str)  # Shows final transpiled/customized query

This approach ensures consistency (same business logic across engines), accessibility (as much as possible, engines work out-of-the-box), and flexibility (custom optimizations where needed).

📬 Feedback / Contributions

Got ideas? Found a bug? Want to contribute a benchmark or engine wrapper? PRs and issues are welcome!

Acknowledgement of Other LakeBench Projects

The LakeBench name is also used by two unrelated academic and research efforts:

• RLGen/LAKEBENCH: A benchmark designed for evaluating vision-language models on multimodal tasks.
• LakeBench: Benchmarks for Data Discovery over Lakes (paper link): A benchmark suite focused on improving data discovery and exploration over large data lakes.

While these projects target very different problem domains — such as machine learning and data discovery — they coincidentally share the same name. This project, focused on ELT benchmarking across lakehouse engines, is not affiliated with or derived from either.