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 the instructions below 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.
Set up interactive shell
To use Delta Lake interactively within the Spark’s Scala/Python shell, you need a local installation of Apache Spark. Depending on whether you want to use Python or Scala, you can set up either PySpark or the Spark shell, respectively.
PySpark
Install or upgrade Pyspark (3.0 or above) by running the following:
pip install --upgrade pyspark
Then, run PySpark with the Delta Lake package and additional configurations:
pyspark --packages io.delta:delta-core_2.12:0.7.0 --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 latest version of Apache Spark (3.0 or above) 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-core_2.12:0.7.0 --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.
Python
For setting up a Python project (e.g., for unit testing), you must start the Spark session first with the Delta Lake package and then import the Python APIs.
spark = pyspark.sql.SparkSession.builder.appName("MyApp") \
.config("spark.jars.packages", "io.delta:delta-core_2.12:0.7.0") \
.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \
.getOrCreate()
from delta.tables import *
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.
data = spark.range(0, 5)
data.write.format("delta").save("/tmp/delta-table")
val data = spark.range(0, 5)
data.write.format("delta").save("/tmp/delta-table")
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 = 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":
df = spark.read.format("delta").load("/tmp/delta-table")
df.show()
val df = spark.read.format("delta").load("/tmp/delta-table")
df.show()
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
data = spark.range(5, 10)
data.write.format("delta").mode("overwrite").save("/tmp/delta-table")
val data = spark.range(5, 10)
data.write.format("delta").mode("overwrite").save("/tmp/delta-table")
df.show()
Dataset<Row> data = 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.
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()
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()
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();
You should see that some of the existing rows have been updated and new rows have been inserted.
For more information on these operations, see Table deletes, updates, and merges.
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.
df = spark.read.format("delta").option("versionAsOf", 0).load("/tmp/delta-table")
df.show()
val df = spark.read.format("delta").option("versionAsOf", 0).load("/tmp/delta-table")
df.show()
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:
streamingDf = spark.readStream.format("rate").load()
stream = streamingDf.selectExpr("value as id").writeStream.format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table")
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")
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.
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.
stream2 = spark.readStream.format("delta").load("/tmp/delta-table").writeStream.format("console").start()
val stream2 = spark.readStream.format("delta").load("/tmp/delta-table").writeStream.format("console").start()
StreamingQuery stream2 = spark.readStream().format("delta").load("/tmp/delta-table").writeStream().format("console").start();