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Qbeast Spark is an extension for Data Lakehouses that enables multi-dimensional filtering and sampling directly on the storage

Features

1. Data Lakehouse - Data lake with ACID properties, thanks to the underlying Delta Lake architecture

2. Multi-column indexing: Filter your data with multiple columns using the Qbeast Format.

3. Improved Sampling operator - Read statistically significant subsets of files.

4. Table Tolerance - Model for sampling fraction and query accuracy trade-off.

Query example with Qbeast

As you can see above, the Qbeast Spark extension allows faster queries with statistically accurate sampling.

Format	Execution Time	Result
Delta	~ 151.3 sec.	37.869383
Qbeast	~ 6.6 sec.	37.856333

In this example, 1% sampling provides the result x22 times faster compared to using Delta format, with an error of 0,034%.

Quickstart

You can run the qbeast-spark application locally on your computer, or using a Docker image we already prepared with the dependencies. You can find it in the Packages section.

Pre: Install Spark

Download Spark 3.4.1 with Hadoop 3.3.4, unzip it, and create the SPARK_HOME environment variable:

ℹ️ Note: You can use Hadoop 2.7 if desired, but you could have some troubles with different cloud providers' storage, read more about it here.

wget https://archive.apache.org/dist/spark/spark-3.4.1/spark-3.4.1-bin-hadoop3.tgz

tar -xzvf spark-3.4.1-bin-hadoop3.tgz

export SPARK_HOME=$PWD/spark-3.4.1-bin-hadoop3

1. Launch a spark-shell

Inside the project folder, launch a spark shell with the required dependencies:

$SPARK_HOME/bin/spark-shell \
--packages io.qbeast:qbeast-spark_2.12:0.5.0,io.delta:delta-core_2.12:2.4.0 \
--conf spark.sql.extensions=io.qbeast.spark.internal.QbeastSparkSessionExtension \
--conf spark.sql.catalog.spark_catalog=io.qbeast.spark.internal.sources.catalog.QbeastCatalog

2. Indexing a dataset

Read the CSV source file placed inside the project.

val csv_df = spark.read.format("csv")
  .option("header", "true")
  .option("inferSchema", "true")
  .load("./src/test/resources/ecommerce100K_2019_Oct.csv")

Indexing the dataset by writing it into the qbeast format, specifying the columns to index.

val tmp_dir = "/tmp/qbeast-spark"

csv_df.write
	.mode("overwrite")
	.format("qbeast")
	.option("columnsToIndex", "user_id,product_id")
	.save(tmp_dir)

SQL Syntax.

You can create a table with Qbeast with the help of QbeastCatalog.

spark.sql(
  "CREATE TABLE student (id INT, name STRING, age INT) " +
    "USING qbeast OPTIONS ('columnsToIndex'='id')")

Use INSERT INTO to add records to the new table. It will update the index in a dynamic fashion when new data is inserted.

spark.sql("INSERT INTO table student SELECT * FROM visitor_students")

3. Load the dataset

Load the newly indexed dataset.

val qbeast_df =
   spark
     .read
     .format("qbeast")
     .load(tmp_dir)

4. Examine the Query plan for sampling

Sampling the data, notice how the sampler is converted into filters and pushed down to the source!

qbeast_df.sample(0.1).explain(true)

Go to the Quickstart or notebook for more details.

5. Interact with the format

Get insights or execute operations to the data using the QbeastTable interface!

import io.qbeast.spark.QbeastTable

val qbeast_table = QbeastTable.forPath(spark, tmp_dir) 

qbeastTable.getIndexMetrics()

qbeastTable.analyze()

Go to QbeastTable documentation for more detailed information.

6. Visualize index

Use Python index visualizer for your indexed table to visually examine index structure and gather sampling metrics.

Dependencies and Version Compatibility

Version	Spark	Hadoop	Delta Lake
0.1.0	3.0.0	3.2.0	0.8.0
0.2.0	3.1.x	3.2.0	1.0.0
0.3.x	3.2.x	3.3.x	1.2.x
0.4.x	3.3.x	3.3.x	2.1.x
0.5.x	3.4.x	3.3.x	2.4.x

Check here for Delta Lake and Apache Spark version compatibility.

Contribution Guide

See Contribution Guide for more information.

License

See LICENSE.

Code of conduct

See Code of conduct