Lake Loader

Lake loader is a tool to benchmark incremental load (writes) to data lakes and warehouses. The tool generates input datasets with configurations to cover different aspects of load patterns - number of records, number of partitions, record size, update to insert ratio, distribution of inserts & updates across partitions and total number of rounds of incremental loads to perform.

The tool consists of two main components:

Change data generator This component takes a specified L pattern and generates rounds of inputs. Each input round has change records, which can be either an insert or an update to an insert in a prior input round.

Incremental Loader The loader component implements best practices for loading data into various open table formats using popular cloud data platforms like AWS EMR, Databricks and Snowflake. Round 0 is specially designed to perform a one-time bulk load using the preferred bulk loading methods for the data platform. Round 1 and above simply perform incremental loads using pre-generated input change records from each round.

Building

Lake loader is built with Java 11 or 17 using Maven.

Run mvn clean package to build.

Formatting

Run mvn scalafmt:format to apply scala formatting.

Engine Compatibility

Lake loader requires spark to generate and load the dataset, compatible with spark3.x including spark3.5

Component Overview

Lake Loader provides two main ways to use its components:

Scala API: Use the components programmatically within spark-shell or notebooks by importing the classes and calling methods
Command-Line Interface (CLI): Use spark-submit with command-line arguments for automated scripts and workflows

Both interfaces support the same parameters and functionality. The parameter reference tables below show both the Scala API parameter names and CLI flags.

ChangeDataGenerator Parameters

The ChangeDataGenerator component generates change data with configurable patterns for benchmarking.

Scala API:

// Constructor
new ChangeDataGenerator(spark: SparkSession, numRounds: Int)

// Main method
def generateWorkload(
  outputPath: String,
  roundsDistribution: List[Long],
  numColumns: Int = 10,
  recordSize: Int = 1024,
  updateRatio: Float = 0.5f,
  totalPartitions: Int = -1,
  partitionDistributionMatrixOpt: Option[List[List[Double]]] = None,
  datagenFileSize: Int = 128 * 1024 * 1024,
  skipIfExists: Boolean = false,
  startRound: Int = 0,
  primaryKeyType: KeyType = KeyType.Random,
  updatePatterns: UpdatePatterns = UpdatePatterns.Uniform,
  numPartitionsToUpdate: Int = -1,
  zipfianShape: Double = 2.93
)

CLI:

spark-submit --class ai.onehouse.lakeloader.ChangeDataGenerator <jar-file> [options]

Parameter Reference

Parameter	CLI Flag	Type	Default	Description
outputPath	`-p`, `--path`	String	required	Output path where generated change data will be stored
numRounds	`--number-rounds`	Int	10	Number of rounds of incremental change data to generate
N/A (CLI only)	`--number-records-per-round`	Long	1000000	Number of records per round (CLI only, uniform across rounds)
roundsDistribution	N/A (CLI only)	List[Long]	computed	Number of records per round (Scala API only, per-round control)
numColumns	`--number-columns`	Int	10	Number of columns in schema of generated data (min: 5)
recordSize	`--record-size`	Int	1024	Record size of generated data in bytes
updateRatio	`--update-ratio`	Double	0.5	Ratio of updates to total records (0.0-1.0)
totalPartitions	`--total-partitions`	Int	-1	Total number of partitions (-1 for unpartitioned)
datagenFileSize	`--datagen-file-size`	Int	134217728	Target data file size in bytes (default: 128MB)
skipIfExists	`--skip-if-exists`	Boolean	false	Skip generation if folder already exists
startRound	`--start-round`	Int	0	Starting round number (for resuming generation)
primaryKeyType	`--primary-key-type`	KeyType	Random	Key generation type: `Random`, `TemporallyOrdered`
updatePatterns	`--update-pattern`	UpdatePatterns	Uniform	Update distribution: `Uniform`, `Zipf`
numPartitionsToUpdate	`--num-partitions-to-update`	Int	-1	Number of partitions to update (-1 for all)
zipfianShape	`--zipfian-shape`	Double	2.93	Shape parameter for Zipf distribution (higher = more skewed)

Notes:

Record count specification: CLI uses --number-records-per-round which applies a uniform count across all rounds. Scala API uses roundsDistribution parameter which allows specifying different record counts for each round (e.g., List(1000000000L, 10000000L, 10000000L, ...) for a large initial load followed by smaller incremental rounds).

IncrementalLoader Parameters

The IncrementalLoader component loads the generated change data into various table formats.

Scala API:

// Constructor
new IncrementalLoader(spark: SparkSession, numRounds: Int)

// Main method
def doWrites(
  inputPath: String,
  outputPath: String,
  format: StorageFormat = StorageFormat.Hudi,
  initialOperation: OperationType = OperationType.BulkInsert,
  operation: OperationType = OperationType.Upsert,
  apiType: ApiType = ApiType.SparkDatasourceApi,
  initialOpts: Map[String, String] = Map.empty,
  opts: Map[String, String] = Map.empty,
  nonPartitioned: Boolean = false,
  experimentId: String = generateRandomString(10),
  startRound: Int = 0,
  catalog: String = "spark_catalog",
  database: String = "default",
  mergeConditionColumns: Seq[String] = Seq.empty,
  mergeMode: MergeMode = MergeMode.UpdateInsert,
  updateColumns: Seq[String] = Seq.empty,
  writeMode: WriteMode = WriteMode.CopyOnWrite,
  asyncCompactionEnabled: Boolean = false,
  compactionFrequencyCommits: Int = 3,
  runFinalCompaction: Boolean = true,
  maxRetries: Int = 5,
  compactionMinFileSize: Long = 104857600,
  compactionTargetFileSize: Long = 125829120
)

CLI:

spark-submit --class ai.onehouse.lakeloader.IncrementalLoader <jar-file> [options]

Parameter Reference

Parameter	CLI Flag	Type	Default	Description
inputPath	`-i`, `--input-path`	String	required	Input path where change data was generated
outputPath	`-o`, `--output-path`	String	required	Output path where table data will be written
numRounds	`--number-rounds`	Int	10	Number of rounds of incremental data to load
format	`--format`	StorageFormat	hudi	Table format: `iceberg`, `delta`, `hudi`, `parquet`
initialOperation	`--initial-operation-type`	OperationType	bulk_insert	Write operation for first batch: `bulk_insert`, `upsert`, `insert`
operation	`--operation-type`	OperationType	upsert	Write operation for subsequent batches: `upsert`, `insert`
apiType	`--api-type`	ApiType	spark-datasource	API type: `spark-datasource`, `spark-sql`
initialOpts	`--initial-options`	Map[String,String]	{}	Format-specific options for first batch (e.g., `key1=value1 key2=value2`)
opts	`--options`	Map[String,String]	{}	Options for subsequent batches
nonPartitioned	`--non-partitioned`	Boolean	false	Whether table is non-partitioned
experimentId	`-e`, `--experiment-id`	String	random	Experiment identifier for tracking
startRound	`--start-round`	Int	0	Starting round for loading (for resuming)
catalog	`--catalog`	String	spark_catalog	Catalog name for table registration
database	`--database`	String	default	Database name for table registration
mergeConditionColumns	`--additional-merge-condition-columns`	Seq[String]	[]	Additional merge condition columns beyond defaults
mergeMode	`--merge-mode`	MergeMode	update-insert	Merge mode: `update-insert`, `delete-insert`
updateColumns	`--update-columns`	Seq[String]	[]	Specific columns to update (empty = all columns)
writeMode	`--write-mode`	WriteMode	copy-on-write	Write mode: `copy-on-write`, `merge-on-read`
asyncCompactionEnabled	`--async-compaction`	Boolean	false	Enable async background compaction
compactionFrequencyCommits	`--compaction-frequency-commits`	Int	3	Schedule compaction every N commits
runFinalCompaction	`--run-final-compaction`	Boolean	true	Run final compaction on shutdown
maxRetries	`--max-retries`	Int	5	Maximum retries for failed ingestion batches
compactionMinFileSize	`--compaction-min-file-size`	Long	104857600	Files smaller than this (bytes) are grouped and rewritten
compactionTargetFileSize	`--compaction-target-file-size`	Long	125829120	Target file size (bytes) produced by compaction
deltaOptimizeWrite	`--delta-optimize-write`	Boolean	true	Enable optimized writes for Delta Lake partitioned tables

Notes:

CLI uses --additional-merge-condition-columns while Scala API uses mergeConditionColumns for the full merge condition list. Default merge columns are [key] for non-partitioned or [key, partition] for partitioned tables.
The --api-type option is only supported with --format hudi. Using spark-sql with other formats will result in an error.
Write Mode: The --write-mode option controls the table type and update/delete behavior for each format:
- Hudi: Sets table type to cow (Copy-On-Write) or mor (Merge-On-Read)
- Delta: Enables deletion vectors when set to merge-on-read
- Iceberg: Sets write.delete.mode, write.update.mode, and write.merge.mode properties

Async Compaction

The IncrementalLoader supports asynchronous background compaction for Merge-On-Read (MOR) tables. This feature allows compaction to run in the background while ingestion continues, improving overall throughput for workloads with frequent updates.

When to use async compaction:

When using --write-mode merge-on-read with Hudi, Delta, or Iceberg formats
For workloads with high update ratios where compaction overhead impacts ingestion performance
When you want to decouple compaction from the critical write path

Configuration options:

--async-compaction: Enable async background compaction (default: false)
--compaction-frequency-commits: Schedule compaction every N commits (default: 3)
--run-final-compaction: Run a final compaction when the loader shuts down (default: true)

Compaction file size options:

--compaction-min-file-size: Files smaller than this threshold (in bytes) will be grouped together and rewritten by compaction. Applies to Delta OPTIMIZE (spark.databricks.delta.optimize.minFileSize) and Iceberg rewrite_data_files (min-file-size-bytes). Default: 104857600 (100MB)
--compaction-target-file-size: Target file size (in bytes) produced by compaction. Applies to Delta OPTIMIZE (spark.databricks.delta.optimize.maxFileSize) and Iceberg rewrite_data_files (target-file-size-bytes). For Iceberg with MOR and async compaction enabled, this also sets the ingestion file size (write.target-file-size-bytes). Default: 125829120 (120MB)

Example with async compaction enabled (Hudi):

spark-submit --class ai.onehouse.lakeloader.IncrementalLoader <jar-file> \
  --input-path s3a://input/data \
  --output-path s3a://output/table \
  --format hudi \
  --write-mode merge-on-read \
  --async-compaction true \
  --compaction-frequency-commits 5

Example with async compaction enabled (Delta/Iceberg with file size tuning):

spark-submit --class ai.onehouse.lakeloader.IncrementalLoader <jar-file> \
  --input-path s3a://input/data \
  --output-path s3a://output/table \
  --format delta \
  --async-compaction true \
  --compaction-frequency-commits 5 \
  --compaction-min-file-size 104857600 \
  --compaction-target-file-size 125829120

Scala API example (Hudi):

loader.doWrites(
  inputPath,
  outputPath,
  format = StorageFormat.Hudi,
  writeMode = WriteMode.MergeOnRead,
  asyncCompactionEnabled = true,
  compactionFrequencyCommits = 5,
  runFinalCompaction = true
)

Scala API example (Delta/Iceberg with file size tuning):

loader.doWrites(
  inputPath,
  outputPath,
  format = StorageFormat.Delta,  // or StorageFormat.Iceberg
  asyncCompactionEnabled = true,
  compactionFrequencyCommits = 5,
  compactionMinFileSize = 100 * 1024 * 1024,
  compactionTargetFileSize = 120 * 1024 * 1024
)

How it works:

When async compaction is enabled, ingestion writes proceed without waiting for compaction
A background service monitors commit counts and triggers compaction every N commits
For Hudi MOR tables, the loader automatically configures optimistic concurrency control to allow concurrent writes and compaction
Compaction metrics (start time, end time, duration, success/failure) are collected and reported in the final summary
If a compaction fails, the service will retry with a configurable delay

Delta Lake Optimized Writes

The IncrementalLoader supports Delta Lake's optimizeWrite feature for partitioned tables. This feature automatically coalesces small files and balances data distribution across output files, preventing skewed large files during write and merge operations.

When to use:

When writing to partitioned Delta tables with uneven data distribution
When merge operations produce skewed large files (e.g., 90GB+ files alongside ~1GB files)
For large-scale workloads (multi-TB) where data skew is common

Configuration:

--delta-optimize-write: Enable optimized writes for Delta Lake partitioned tables (default: true)

How it works:

For Upsert (merge) operations: Sets session config spark.databricks.delta.optimizeWrite.enabled to balance data distribution and prevent skewed large files

Example:

spark-submit --class ai.onehouse.lakeloader.IncrementalLoader <jar-file> \
  --input-path s3a://input/data \
  --output-path s3a://output/table \
  --format delta \
  --delta-optimize-write true

To disable (not recommended for partitioned tables):

  --delta-optimize-write false

Batch Retry Logic

The IncrementalLoader includes automatic retry logic for failed ingestion batches. This improves reliability for long-running benchmarks where transient failures might occur.

Configuration:

--max-retries: Maximum number of retries for failed ingestion batches (default: 5)

Behavior:

When a batch fails, the loader will retry up to maxRetries times before giving up
Each failed attempt is recorded with full metrics (start time, end time, duration, error message)
Both successful and failed batch metrics are included in the final summary
If all retries are exhausted, the original exception is re-thrown

Example:

spark-submit --class ai.onehouse.lakeloader.IncrementalLoader <jar-file> \
  --input-path s3a://input/data \
  --output-path s3a://output/table \
  --format iceberg \
  --max-retries 3

Metrics output includes:

BATCH METRICS (N runs):
  Round 0: 1234ms ... (SUCCESS)
  Round 1: 567ms ... (FAILED)
  Round 1: 890ms ... (SUCCESS)
  ...
  Successful runs: X
  Failed runs: Y

Usage Examples

Dataset Generation Examples

This section demonstrates how to use the ChangeDataGenerator to create datasets for different table types: FACT tables (Zipfian pattern for updates), DIM tables (random pattern for updates), and EVENTS tables (Append-only).

NOTE: Examples below use Scala API in spark-shell. For CLI usage with spark-submit, use the class name ai.onehouse.lakeloader.ChangeDataGenerator and refer to the parameter table above for command-line flags.

Fact table arguments

The following generates dataset for a 1TB FACT table with:

365 partitions based on date
1B records
40 column schema
record size of almost 1KB
25% of an incremental batch is updates, 75% are new records.
updates are distributed across partitions in a zipf distribution, with most updates in the latest partitions.
updates are spread across last 90 partitions, while insert records are written to last 2 partitions with equal spread.

import ai.onehouse.lakeloader.ChangeDataGenerator
import ai.onehouse.lakeloader.configs.UpdatePatterns

val output_path = "file:///<output_path>"
val numRounds = 20

val partitionDistribution:List[List[Double]] = List(List.fill(365)(1.0/365)) ++ List.fill(numRounds-1)(List(0.5, 0.5) ++ List.fill(363)(0.0))

val datagen = new ChangeDataGenerator(spark, numRounds = numRounds)
datagen.generateWorkload(output_path,
                         roundsDistribution = List(1000000000L) ++ List.fill(numRounds-1)(10000000L),
                         numColumns = 40,
                         recordSize = 1000,
                         updateRatio = 0.25f,
                         totalPartitions = 365,
                         partitionDistributionMatrixOpt = Some(partitionDistribution),
                         updatePatterns=UpdatePatterns.Zipf,
                         numPartitionsToUpdate=90)
)

Dim table arguments

The following generates dateset for a 100GB DIM table with

No partitions or un-partitioned table
100M records
99 column schema
record size of almost 1KB
50% of an incremental batch is updates, 50% are new records.
updates are randomly distributed across the entire dataset.

import ai.onehouse.lakeloader.ChangeDataGenerator
import ai.onehouse.lakeloader.configs.UpdatePatterns

val output_path = "file:///<input_path>"
val numRounds = 20

val datagen = new ChangeDataGenerator(spark, numRounds = numRounds)
datagen.generateWorkload(output_path,
                         roundsDistribution = List(100000000L) ++ List.fill(numRounds-1)(500000L),
                         numColumns = 99,
                         recordSize = 1000,
                         updateRatio = 0.5f,
                         totalPartitions = 1,
                         updatePatterns = UpdatePatterns.Uniform,
                         numPartitionsToUpdate = 1)

Events table arguments

The following generates dateset for a 2TB EVENTS table with

1095 partitions based on date
10B records
30 column schema
record size of almost 200Bytes
100% of incremental batch are new records (No updates).
All records in an incremental batch will be generated for the latest partition.

import ai.onehouse.lakeloader.ChangeDataGenerator
import ai.onehouse.lakeloader.configs.UpdatePatterns

val output_path = "file:///<output_path>"
val numRounds = 20
val numPartitions = 1095

val partitionDistribution:List[List[Double]] = List(List.fill(numPartitions)(1.0/numPartitions)) ++ List.fill(numRounds-1)(List(1.0) ++ List.fill(numPartitions-1)(0.0))

val datagen = new ChangeDataGenerator(spark, numRounds = numRounds)
datagen.generateWorkload(output_path,
                         roundsDistribution = List(10000000000L) ++ List.fill(numRounds-1)(50000000L),
                         numColumns = 30,
                         recordSize = 200,
                         updateRatio = 0.0f,
                         totalPartitions = numPartitions,
                         partitionDistributionMatrixOpt = Some(partitionDistribution))

Incremental Loading Examples

This section shows how to use the IncrementalLoader component to load the generated change data into various table formats.

NOTE: Examples below use Scala API in spark-shell. For CLI usage with spark-submit, use the class name ai.onehouse.lakeloader.IncrementalLoader and refer to the parameter table above for command-line flags.

EMR

Provision a cluster with the latest EMR version 7.5.0. Using the spark-shell, you need to configure the following additional configs besides the typical spark configs:

  --jars /usr/share/aws/iceberg/lib/iceberg-spark3-runtime.jar
  --conf spark.sql.catalog.my_catalog=org.apache.iceberg.spark.SparkCatalog \
  --conf spark.sql.catalog.my_catalog.warehouse=s3://hudi-benchmark-source/icebergCatalog/ \
  --conf spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog \
  --conf spark.sql.catalog.my_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO \
  --conf spark.sql.defaultCatalog=my_catalog \
  --conf spark.sql.catalog.my_catalog.http-client.apache.max-connections=5000 \
  --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions

Within spark-shell, run the following command for FACT tables.

import ai.onehouse.lakeloader.IncrementalLoader
import ai.onehouse.lakeloader.configs.StorageFormat
import ai.onehouse.lakeloader.configs.OperationType

val experimentId = "emr_fact"
val numRounds = 10
val inputPath = "s3a://input/fact-data"
val targetPath = "s3a://output/emr-fact"

val loader = new IncrementalLoader(spark, numRounds = numRounds)

var opts = Map("write.parquet.compression-codec" -> "snappy")
loader.doWrites(inputPath,
  targetPath,
  format = StorageFormat.Iceberg,
  operation = OperationType.Upsert,
  opts = opts,
  experimentId = experimentId)

Run the following for DIM tables since they are non-partitioned.

import ai.onehouse.lakeloader.IncrementalLoader
import ai.onehouse.lakeloader.configs.StorageFormat
import ai.onehouse.lakeloader.configs.OperationType

val experimentId = "emr_dim"
val numRounds = 10
val inputPath = "s3a://input/dim-data"
val targetPath = "s3a://output/emr-dim"

val loader = new IncrementalLoader(spark, numRounds = numRounds)

var opts = Map("write.parquet.compression-codec" -> "snappy")
loader.doWrites(inputPath,
  targetPath,
  format = StorageFormat.Iceberg,
  operation = OperationType.Upsert,
  opts = opts,
  nonPartitioned = true,
  experimentId = experimentId)

Run the following for EVENT tables since they are insert-only.

import ai.onehouse.lakeloader.IncrementalLoader
import ai.onehouse.lakeloader.configs.StorageFormat
import ai.onehouse.lakeloader.configs.OperationType

val experimentId = "emr_event"
val numRounds = 10
val inputPath = "s3a://input/event-data"
val targetPath = "s3a://output/emr-event"

val loader = new IncrementalLoader(spark, numRounds = numRounds)

var opts = Map("write.parquet.compression-codec" -> "snappy")
loader.doWrites(inputPath,
  targetPath,
  format = StorageFormat.Iceberg,
  operation = OperationType.Insert,
  opts = opts,
  experimentId = experimentId)

Databricks

Provision a recent Databricks cluster with or without native acceleration (photon) enabled. Use the following (optional) spark configs to disable deletion vectors for delta to test with COPY_ON_WRITE, snappy compression and disable parquet file caching.

  .config("spark.databricks.delta.properties.defaults.enableDeletionVectors", "false")
  .config("spark.sql.parquet.compression.codec", "snappy")
  .config("spark.databricks.io.cache.enabled", "false")

Within a notebook, run the following command for FACT tables.

import ai.onehouse.lakeloader.IncrementalLoader
import ai.onehouse.lakeloader.configs.StorageFormat
import ai.onehouse.lakeloader.configs.OperationType

val experimentId = "dbr_fact"
val numRounds = 10
val inputPath = "s3a://input/fact-data"
val targetPath = "s3a://output/dbr-fact"

val loader = new IncrementalLoader(spark, numRounds = numRounds)

var opts = Map("write.parquet.compression-codec" -> "snappy")
loader.doWrites(inputPath,
  targetPath,
  format = StorageFormat.Delta,
  operation = OperationType.Upsert,
  opts = opts,
  experimentId = experimentId)

Run the following for DIM tables since they are non-partitioned.

import ai.onehouse.lakeloader.IncrementalLoader
import ai.onehouse.lakeloader.configs.StorageFormat
import ai.onehouse.lakeloader.configs.OperationType

val experimentId = "dbr_dim"
val numRounds = 10
val inputPath = "s3a://input/dim-data"
val targetPath = "s3a://output/dbr-dim"

val loader = new IncrementalLoader(spark, numRounds = numRounds)

var opts = Map("write.parquet.compression-codec" -> "snappy")
loader.doWrites(inputPath,
  targetPath,
  format = StorageFormat.Delta,
  operation = OperationType.Upsert,
  opts = opts,
  nonPartitioned = true,
  experimentId = experimentId)

Run the following for EVENT tables since they are insert-only.

import ai.onehouse.lakeloader.IncrementalLoader
import ai.onehouse.lakeloader.configs.StorageFormat
import ai.onehouse.lakeloader.configs.OperationType

val experimentId = "dbr_event"
val numRounds = 10
val inputPath = "s3a://input/event-data"
val targetPath = "s3a://output/dbr-event"

val loader = new IncrementalLoader(spark, numRounds = numRounds)

var opts = Map("write.parquet.compression-codec" -> "snappy")
loader.doWrites(inputPath,
  targetPath,
  format = StorageFormat.Delta,
  operation = OperationType.Insert,
  opts = opts,
  experimentId = experimentId)

Snowflake

In the case of Snowflake warehouse, we have added 3 scripts to help run the FACT, DIM and EVENT table benchmarks using a medium cluster size. The files are under the scripts/snowflake directory

snowflake_fact_incremental_load_sql.txt
snowflake_dim_incremental_load_sql.txt
snowflake_events_incremental_load_sql.txt

Name		Name	Last commit message	Last commit date
Latest commit History 78 Commits
scripts/snowflake		scripts/snowflake
src/main		src/main
.gitignore		.gitignore
.scalafmt.conf		.scalafmt.conf
LICENSE		LICENSE
README.md		README.md
pom.xml		pom.xml

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