Quickstart

This guide helps you quickly explore the main features of Delta Lake. It provides code snippets that show how to read from and write to Delta tables from interactive, batch, and streaming queries.

Set up Apache Spark with Delta Lake

Follow these instructions to set up Delta Lake with Spark. You can run the steps in this guide on your local machine in the following two ways:

• Run interactively: Start the Spark shell (Scala or Python) with Delta Lake and run the code snippets interactively in the shell.

• Run as a project: Set up a Maven or SBT project (Scala or Java) with Delta Lake, copy the code snippets into a source file, and run the project. Alternatively, you can use the examples provided in the Github repository.

Important!

For all of the following instructions, make sure to install the correct version of Spark or PySpark that is compatible with Delta Lake 3.3.0. See the release compatibility matrix for details.

Prerequisite: set up Java

As mentioned in the official Apache Spark installation instructions here, make sure you have a valid Java version installed (8, 11, or 17) and that Java is configured correctly on your system using either the system PATH or JAVA_HOME environmental variable.

Windows Users

Windows users should follow the instructions in this blog, making sure to use the correct version of Apache Spark that is compatible with Delta Lake 3.3.0.

To use Delta Lake interactively within the Spark SQL, Scala, or Python shell, you need a local installation of Apache Spark. Depending on whether you want to use SQL, Python, or Scala, you can set up either the SQL, PySpark, or Spark shell, respectively.

Spark SQL Shell

Spark SQL Shell

Download the compatible version of Apache Spark by following instructions from Downloading Spark, either using pip or by downloading and extracting the archive and running spark-sql in the extracted directory.

bin/spark-sql --packages io.delta:delta-spark_2.12:3.3.0 --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"

PySpark Shell

1. Install the PySpark version that is compatible with the Delta Lake version by running the following:

pip install pyspark==<compatible-spark-version>

2. Run PySpark with the Delta Lake package and additional configurations:

pyspark --packages io.delta:delta-spark_2.12:3.3.0 --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"

Spark Scala Shell

Download the compatible version of Apache Spark by following instructions from Downloading Spark, either using pip or by downloading and extracting the archive and running spark-shell in the extracted directory.

bin/spark-shell --packages io.delta:delta-spark_2.12:3.3.0 --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"

Set up project

If you want to build a project using Delta Lake binaries from Maven Central Repository, you can use the following Maven coordinates.

Maven

You include Delta Lake in your Maven project by adding it as a dependency in your POM file. Delta Lake compiled with Scala 2.12.

<dependency>

<groupId>io.delta</groupId>
<artifactId>delta-spark_2.12</artifactId>
<version>3.3.0</version>
</dependency>

SBT

You include Delta Lake in your SBT project by adding the following line to your build.sbt file:

libraryDependencies += "io.delta" %% "delta-spark" % "3.3.0"

Python

To set up a Python project (for example, for unit testing), you can install Delta Lake using pip install delta-spark==3.3.0 and then configure the SparkSession with the configure_spark_with_delta_pip() utility function in Delta Lake.

import pyspark

from delta import *

builder = pyspark.sql.SparkSession.builder.appName("MyApp") \ .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \ .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog")

spark = configure_spark_with_delta_pip(builder).getOrCreate()

Create a table

To create a Delta table, write a DataFrame out in the delta format. You can use existing Spark SQL code and change the format from parquet, csv, json, and so on, to delta.

SQL

CREATE TABLE delta.`/tmp/delta-table` USING DELTA AS SELECT col1 as id FROM VALUES 0,1,2,3,4;

Python

data = spark.range(0, 5)
data.write.format("delta").save("/tmp/delta-table")

Scala

val data = spark.range(0, 5)
data.write.format("delta").save("/tmp/delta-table")

Java

import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

SparkSession spark = ... // create SparkSession

Dataset<Row> data = spark.range(0, 5); data.write().format("delta").save("/tmp/delta-table");

These operations create a new Delta table using the schema that was inferred from your DataFrame. For the full set of options available when you create a new Delta table, see Create a table and Write to a table.

Note

This quickstart uses local paths for Delta table locations. For configuring HDFS or cloud storage for Delta tables, see Storage configuration.

Read data

You read data in your Delta table by specifying the path to the files: /tmp/delta-table:

SQL

SELECT * FROM delta.`/tmp/delta-table`;

Python

df = spark.read.format("delta").load("/tmp/delta-table")
df.show()

Scala

val df = spark.read.format("delta").load("/tmp/delta-table")
df.show()

Java

Dataset<Row> df = spark.read().format("delta").load("/tmp/delta-table");
df.show();

Update table data

Delta Lake supports several operations to modify tables using standard DataFrame APIs. This example runs a batch job to overwrite the data in the table:

Overwrite

SQL

INSERT OVERWRITE delta.`/tmp/delta-table` SELECT col1 as id FROM VALUES 5,6,7,8,9;

Python

data = spark.range(5, 10)
data.write.format("delta").mode("overwrite").save("/tmp/delta-table")

Scala

val data = spark.range(5, 10)
data.write.format("delta").mode("overwrite").save("/tmp/delta-table")
df.show()

Java

Dataset<Row> data = spark.range(5, 10);
data.write().format("delta").mode("overwrite").save("/tmp/delta-table");

If you read this table again, you should see only the values 5-9 you have added because you overwrote the previous data.

Conditional update without overwrite

Delta Lake provides programmatic APIs to conditional update, delete, and merge (upsert) data into tables. Here are a few examples.

SQL

-- Update every even value by adding 100 to it
UPDATE delta.`/tmp/delta-table` SET id = id + 100 WHERE id % 2 == 0;

-- Delete every even value DELETE FROM delta.`/tmp/delta-table` WHERE id % 2 == 0;

-- Upsert (merge) new data CREATE TEMP VIEW newData AS SELECT col1 AS id FROM VALUES 1,3,5,7,9,11,13,15,17,19;

MERGE INTO delta.`/tmp/delta-table` AS oldData USING newData ON oldData.id = newData.id WHEN MATCHED THEN UPDATE SET id = newData.id WHEN NOT MATCHED THEN INSERT (id) VALUES (newData.id);

SELECT * FROM delta.`/tmp/delta-table`;

Python

from delta.tables import *
from pyspark.sql.functions import *

deltaTable = DeltaTable.forPath(spark, "/tmp/delta-table")

# Update every even value by adding 100 to it

deltaTable.update( condition = expr("id % 2 == 0"), set = { "id": expr("id + 100") })

# Delete every even value

deltaTable.delete(condition = expr("id % 2 == 0"))

# Upsert (merge) new data

newData = spark.range(0, 20)

deltaTable.alias("oldData") \ .merge( newData.alias("newData"), "oldData.id = newData.id") \ .whenMatchedUpdate(set = { "id": col("newData.id") }) \ .whenNotMatchedInsert(values = { "id": col("newData.id") }) \ .execute()

deltaTable.toDF().show()

Scala

import io.delta.tables._
import org.apache.spark.sql.functions._

val deltaTable = DeltaTable.forPath("/tmp/delta-table")

// Update every even value by adding 100 to it deltaTable.update( condition = expr("id % 2 == 0"), set = Map("id" -> expr("id + 100")))

// Delete every even value deltaTable.delete(condition = expr("id % 2 == 0"))

// Upsert (merge) new data val newData = spark.range(0, 20).toDF

deltaTable.as("oldData") .merge( newData.as("newData"), "oldData.id = newData.id") .whenMatched .update(Map("id" -> col("newData.id"))) .whenNotMatched .insert(Map("id" -> col("newData.id"))) .execute()

deltaTable.toDF.show()

Java

import io.delta.tables.*;
import org.apache.spark.sql.functions;
import java.util.HashMap;

DeltaTable deltaTable = DeltaTable.forPath("/tmp/delta-table");

// Update every even value by adding 100 to it deltaTable.update( functions.expr("id % 2 == 0"), new HashMap<String, Column>() {{
  put("id", functions.expr("id + 100"));
}} );

// Delete every even value deltaTable.delete(condition = functions.expr("id % 2 == 0"));

// Upsert (merge) new data Dataset<Row> newData = spark.range(0, 20).toDF();

deltaTable.as("oldData") .merge( newData.as("newData"), "oldData.id = newData.id") .whenMatched() .update( new HashMap<String, Column>() {{
    put("id", functions.col("newData.id"));
  }}) .whenNotMatched() .insertExpr( new HashMap<String, Column>() {{
    put("id", functions.col("newData.id"));
  }}) .execute();

deltaTable.toDF().show();

Read older versions of data using time travel

You can query previous snapshots of your Delta table by using time travel. If you want to access the data that you overwrote, you can query a snapshot of the table before you overwrote the first set of data using the versionAsOf option.

SQL

SELECT * FROM delta.`/tmp/delta-table` VERSION AS OF 0;

Python

df = spark.read.format("delta").option("versionAsOf", 0).load("/tmp/delta-table")
df.show()

Scala

val df = spark.read.format("delta").option("versionAsOf", 0).load("/tmp/delta-table")
df.show()

Java

Dataset<Row> df = spark.read().format("delta").option("versionAsOf", 0).load("/tmp/delta-table");
df.show();

You should see the first set of data, from before you overwrote it. Time travel takes advantage of the power of the Delta Lake transaction log to access data that is no longer in the table. Removing the version 0 option (or specifying version 1) would let you see the newer data again. For more information, see Query an older snapshot of a table (time travel).

Write a stream of data to a table

You can also write to a Delta table using Structured Streaming. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table:

Python

streamingDf = spark.readStream.format("rate").load()
stream = streamingDf.selectExpr("value as id").writeStream.format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table")

Scala

val streamingDf = spark.readStream.format("rate").load()
val stream = streamingDf.select($"value" as "id").writeStream.format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table")

Java

import org.apache.spark.sql.streaming.StreamingQuery;

Dataset<Row> streamingDf = spark.readStream().format("rate").load(); StreamingQuery stream = streamingDf.selectExpr("value as id").writeStream().format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table");

While the stream is running, you can read the table using the earlier commands.

Note

If you’re running this in a shell, you may see the streaming task progress, which make it hard to type commands in that shell. It may be useful to start another shell in a new terminal for querying the table.

You can stop the stream by running stream.stop() in the same terminal that started the stream.

For more information about Delta Lake integration with Structured Streaming, see Table streaming reads and writes. See also the Structured Streaming Programming Guide on the Apache Spark website.

Read a stream of changes from a table

While the stream is writing to the Delta table, you can also read from that table as streaming source. For example, you can start another streaming query that prints all the changes made to the Delta table. You can specify which version Structured Streaming should start from by providing the startingVersion or startingTimestamp option to get changes from that point onwards. See Structured Streaming for details.

Python

stream2 = spark.readStream.format("delta").load("/tmp/delta-table").writeStream.format("console").start()

Scala

val stream2 = spark.readStream.format("delta").load("/tmp/delta-table").writeStream.format("console").start()

Java

StreamingQuery stream2 = spark.readStream().format("delta").load("/tmp/delta-table").writeStream().format("console").start();