Spark via Iceberg¶

To access Nessie on Iceberg from a spark cluster make sure the spark.jars spark option is set to include a jar of the iceberg spark runtime, or the spark.jars.packages spark option is set to include a Maven coordinate of the iceberg spark runtime.

The iceberg-spark-runtime fat jars are distributed by the Apache Iceberg project and contains all Apache Iceberg libraries required for operation, including the built-in Nessie Catalog.

The nessie-spark-extensions jars are distributed by the Nessie project and contain SQL extensions that allow you to manage your tables with nessie’s git-like syntax.

In pyspark, usage would look like…

SparkSession.builder
    .config('spark.jars.packages',
            'org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.5.2')
    ... rest of spark config
    .getOrCreate()

…or if using the nessie extensions…

SparkSession.builder
    .config('spark.jars.packages',
            'org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.5.2,org.projectnessie.nessie-integrations:nessie-spark-extensions-3.3_2.12:0.100.2')
    ... rest of spark config
    .getOrCreate()

The docs for the Java API in Iceberg explain how to use a Catalog. The only change is that a Nessie catalog should be instantiated

Catalog catalog = new NessieCatalog(spark.sparkContext().hadoopConfiguration())

catalog = jvm.NessieCatalog(sc._jsc.hadoopConfiguration())

Configuration¶

The Nessie Catalog needs the following parameters set in the Spark/Hadoop config.

These are set as follows in code (or through other methods as described here)

In these examples, spark.jars.packages is configured for Spark 3.3.x. Consult the table above to find the version of that correspond to your Spark deployment.

// Full url of the Nessie API endpoint to nessie
String url = "http://localhost:19120/api/v1";
// Where to store nessie tables
String fullPathToWarehouse = ...;
// The ref or context that nessie will operate on
// (if different from default branch).
// Can be the name of a Nessie branch or tag name.
String ref = "main";
// Nessie authentication type (NONE, BEARER, OAUTH2 or AWS)
String authType = "NONE";

    // for a local spark instance
    conf.set("spark.jars.packages", "org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.5.2,org.projectnessie.nessie-integrations:nessie-spark-extensions-3.3_2.12:0.100.2")
        .set("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions,org.projectnessie.spark.extensions.NessieSparkSessionExtensions")
        .set("spark.sql.catalog.nessie.uri", url)
        .set("spark.sql.catalog.nessie.ref", ref)
        .set("spark.sql.catalog.nessie.authentication.type", authType)
        .set("spark.sql.catalog.nessie.catalog-impl", "org.apache.iceberg.nessie.NessieCatalog")
        .set("spark.sql.catalog.nessie.warehouse", fullPathToWarehouse)
        .set("spark.sql.catalog.nessie", "org.apache.iceberg.spark.SparkCatalog");
    spark = SparkSession.builder()
                        .master("local[2]")
                        .config(conf)
                        .getOrCreate();

# Full url of the Nessie API endpoint to nessie
url = "http://localhost:19120/api/v1"
# Where to store nessie tables
full_path_to_warehouse = ...
# The ref or context that nessie will operate on (if different from default branch).
# Can be the name of a Nessie branch or tag name.
ref = "main"
# Nessie authentication type (NONE, BEARER, OAUTH2 or AWS)
auth_type = "NONE"

    spark = SparkSession.builder \
            .config("spark.jars.packages","org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.5.2,org.projectnessie.nessie-integrations:nessie-spark-extensions-3.3_2.12:0.100.2") \
            .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions,org.projectnessie.spark.extensions.NessieSparkSessionExtensions") \
            .config("spark.sql.catalog.nessie.uri", url) \
            .config("spark.sql.catalog.nessie.ref", ref) \
            .config("spark.sql.catalog.nessie.authentication.type", auth_type) \
            .config("spark.sql.catalog.nessie.catalog-impl", "org.apache.iceberg.nessie.NessieCatalog") \
            .config("spark.sql.catalog.nessie.warehouse", full_path_to_warehouse) \
            .config("spark.sql.catalog.nessie", "org.apache.iceberg.spark.SparkCatalog") \
            .getOrCreate()

All configuration for the Nessie catalog exists below this spark.sql.catalog.nessie configuration namespace. The catalog name is not important, it is important that the required options are all given below the catalog name.

The following properties are required in Spark when creating the Nessie Catalog (replace <catalog_name> with the name of your catalog):

• spark.sql.catalog.<catalog_name>.uri : The location of the Nessie server.
• spark.sql.catalog.<catalog_name>.ref : The default Nessie branch that the iceberg catalog will use.
• spark.sql.catalog.<catalog_name>.authentication.type : The authentication type to be used, set to NONE by default. Please refer to the Configuration and authentication in Tools docs for more info.
• spark.sql.catalog.<catalog_name>.catalog-impl : This must be org.apache.iceberg.nessie.NessieCatalog in order to tell Spark to use Nessie catalog implementation.
• spark.sql.catalog.<catalog_name>.warehouse : The location where to store Iceberg tables managed by Nessie catalog.
• spark.sql.catalog.<catalog_name> : This must be org.apache.iceberg.spark.SparkCatalog. This is a Spark option to set the catalog <catalog_name> to be managed by Nessie’s Catalog implementation.

Writing¶

Iceberg Catalog APIs can be used for creating the table as follows:

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// first instantiate the catalog
NessieCatalog catalog = new NessieCatalog();
catalog.setConf(sc.hadoopConfiguration());
// other catalog properties can be added based on the requirement. For example, "io-impl","authentication.type", etc.
catalog.initialize("nessie", ImmutableMap.of(
    "ref", ref,
    "uri", url,
    "warehouse", pathToWarehouse));

// Creating table by first creating a table name with namespace
TableIdentifier region_name = TableIdentifier.parse("testing.region");

// next create the schema
Schema region_schema = Schema([
  Types.NestedField.optional(1, "R_REGIONKEY", Types.LongType.get()),
  Types.NestedField.optional(2, "R_NAME", Types.StringType.get()),
  Types.NestedField.optional(3, "R_COMMENT", Types.StringType.get()),
]);

// and the partition
PartitionSpec region_spec = PartitionSpec.unpartitioned();

// finally create the table
catalog.createTable(region_name, region_schema, region_spec);

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sc = spark.sparkContext
jvm = sc._gateway.jvm

# import jvm libraries for iceberg catalogs and schemas
java_import(jvm, "org.projectnessie.iceberg.NessieCatalog")
java_import(jvm, "org.apache.iceberg.catalog.TableIdentifier")
java_import(jvm, "org.apache.iceberg.Schema")
java_import(jvm, "org.apache.iceberg.types.Types")
java_import(jvm, "org.apache.iceberg.PartitionSpec")

# first instantiate the catalog
catalog = jvm.NessieCatalog()
catalog.setConf(sc._jsc.hadoopConfiguration())
# other catalog properties can be added based on the requirement. For example, "io-impl","authentication.type", etc.
catalog.initialize("nessie", {"ref": ref,
    "uri": url,
    "warehouse": pathToWarehouse})

# Creating table by first creating a table name with namespace
region_name = jvm.TableIdentifier.parse("testing.region")

# next create the schema
region_schema = jvm.Schema([
  jvm.Types.NestedField.optional(
    1, "R_REGIONKEY", jvm.Types.LongType.get()
  ),
  jvm.Types.NestedField.optional(
    2, "R_NAME", jvm.Types.StringType.get()
  ),
  jvm.Types.NestedField.optional(
    3, "R_COMMENT", jvm.Types.StringType.get()
  ),
])

# and the partition
region_spec = jvm.PartitionSpec.unpartitioned()

# finally create the table
region_table = catalog.createTable(region_name, region_schema, region_spec)

When looking at the Python code above, lines 1-11 are importing jvm objects into pyspark. Lines 12-25 create the table name, schema and partition spec. These actions will be familiar to seasoned iceberg users and are wholly iceberg operations. Line 29 is where our initial iceberg metadata is finally written to disk and a commit takes place on Nessie.

Now that we have created an Iceberg table in nessie we can write to it. The iceberg DataSourceV2 allows for either overwrite or append mode in a standard spark.write.

Spark support is constantly evolving. See the iceberg docs for an up-to-date support table.

Spark3¶

Spark3 table creation/insertion is as follows:

regionDf = spark.read().load('data/region.parquet');
//create
regionDf.writeTo("nessie.testing.region").create();
//append
regionDf.writeTo("nessie.testing.region").append();
//overwrite partition
regionDf.writeTo("nessie.testing.region").overwritePartitions();

region_df = spark.read.load("data/region.parquet")
region_df.write.format("iceberg").mode("overwrite") \
    .save("nessie.testing.region")

CREATE NAMESPACE nessie.testing;

CREATE TABLE nessie.testing.city (
    C_CITYKEY BIGINT, C_NAME STRING, N_NATIONKEY BIGINT, C_COMMENT STRING
) USING iceberg PARTITIONED BY (N_NATIONKEY)
-- AS SELECT .. can be added to the sql statement to perform a CTAS

INSERT INTO nessie.testing.city VALUES (1, 'a', 1, 'comment')

The full list of operations can be found here. Everything that Iceberg supports the Nessie Iceberg Catalog also supports.

Reading¶

To read a Nessie table in iceberg simply:

regionDf = spark.table("nessie.testing.region");

region_df = spark.read.format("iceberg").load("nessie.testing.region")

SELECT * FROM nessie.testing.city

-- Read from the `etl` branch
SELECT * FROM nessie.testing.`city@etl`

The examples above all use the default branch defined on initialisation. There are several ways to reference specific branches or hashes from within a read statement. We will take a look at a few now from pyspark3, the rules are the same across all environments though. The general pattern is <table>@<branch> or <table>#<hash> or <table>@<branch>#<hash>. Table must be present and either branch and/or hash are optional. We will throw an error if branch or hash don’t exist. Branch or hash references in the table name will override passed options and the settings in the Spark/Hadoop configs.

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# read from branch dev
spark.read().format("iceberg").load("testing.region@dev")
# read specifically from hash
spark.read().format("iceberg").load("testing.region#<hash>")
# read specifically from hash in dev branch
spark.read().format("iceberg").load("testing.region@dev#<hash>")

spark.sql("SELECT * FROM nessie.testing.`region@dev`")
spark.sql("SELECT * FROM nessie.testing.`region#<hash>`")
spark.sql("SELECT * FROM nessie.testing.`region@dev#<hash>`")

Notice in the SQL statements the <table>@<branch> or <table>#<hash> or <table>@<branch>#<hash> must be escaped separately from namespace or catalog arguments.

Future versions may add the ability to specify a timestamp to query the data at a specific point in time (time-travel). In the meantime the history can be viewed on the command line or via the python client and a specific hash based on commit time can be extracted for use in the spark catalog. It is recommended to use the time-travel features of Nessie over the Iceberg features as Nessie history is consistent across the entire database.