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Accessing data in S3 with Spark

In this guide we walk through the process of configuring an Apache Spark session to work with data files stored in Amazon S3 and version history in a local Nessie Server.

Docker is used at the runtime environments for Nessie. Spark is assumed to be installed locally.

Setting up Nessie Server

Start the Nessie server container from the projectnessie/nessie Docker image in default mode.

docker run -p 19120:19120 projectnessie/nessie:latest

Note: this example will run the Nessie Server using in-memory storage for table metadata. If/when the container is deleted, Nessie data about table changes will be lost, yet the data files in S3 will remain.

Setting up Spark Session

Configure an AWS profile (e.g. called demo) in ~/.aws/credentials (or other location appropriate for your OS) and export the profile name in the AWS_PROFILE environment variable. For example:

export AWS_PROFILE=demo

Create an S3 bucket of your own. This guide uses the bucket name spark-demo1.

Start a Spark session:

spark-sql \
 --packages \
org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:0.13.1,\,\ \
 --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions  \
 --conf spark.sql.catalog.nessie=org.apache.iceberg.spark.SparkCatalog \
 --conf spark.sql.catalog.nessie.warehouse=s3://spark-demo1 \
 --conf spark.sql.catalog.nessie.catalog-impl=org.apache.iceberg.nessie.NessieCatalog \
 --conf \
 --conf spark.sql.catalog.nessie.uri=http://localhost:19120/api/v1 \
 --conf spark.sql.catalog.nessie.ref=main \
 --conf spark.sql.catalog.nessie.cache-enabled=false

Note: spark-demo1 is the name of the S3 bucket that will hold table data files.

Note: the --packages option lists modules required for Iceberg to write data files into S3. Please refer to Iceberg documentation for the most up-to-date information on how to connect Iceberg to S3.

Note: the word nessie in configuration property names is the name of the Nessie catalog in the Spark session. A different name can be chosen according the user’s liking.

Then, in spark-sql issue a use statement to make nessie the current catalog:

spark-sql> use nessie

This command will establish a connection to the Nessie Server. When it is done, it will be possible to create tables and run DML. For example:

spark-sql> CREATE TABLE demo (id bigint, data string);
Time taken: 1.615 seconds
spark-sql> show tables;
Time taken: 0.425 seconds, Fetched 1 row(s)
spark-sql> INSERT INTO demo (id, data) VALUES (1, 'a');
Time taken: 4.017 seconds
spark-sql> SELECT * FROM demo;
1   a
Time taken: 3.225 seconds, Fetched 1 row(s)

Branches, merges and other git-like commands can be run as well, as explained in the Getting Started guide.

Note: The above example uses the spark-sql shell, but the same configuration options apply to spark-shell.


This example uses implicit AWS authentication via credentials configured in a credentials file plus the AWS_PROFILE environment variable.

The Nessie Server in this example does not require authentication.

If the Nessie Server runs with authentication enabled, additional configuration parameters will be required in the Spark session. Please refer to the Authentication in Tools section for details.