How to write data to Apache Iceberg tables using Spark SQL? - apache-spark

Question

I am trying to familiarize myself with Apache Iceberg and I'm having some trouble understanding how to write some external data to a table using Spark SQL.
I have a file, one.csv, sitting in a directory, /data
my Iceberg catalog is configured to point to this directory, /warehouse
I want to write this one.csv to an Apache Iceberg table (preferably using Spark SQL)
Is it even possible to read external data using Spark SQL? And then write it to the iceberg tables? Do I have to use scala or python to do this? I've been through the Iceberg and Spark 3.0.1 documentation a bunch but maybe I'm missing something.
Code Update
Here is some code that I hope will help
spark.conf.set("spark.sql.catalog.spark_catalog", "org.apache.iceberg.spark.SparkSessionCatalog")
spark.conf.set("spark.sql.catalog.spark_catalog.type", "hive")
spark.conf.set("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog")
spark.conf.set("spark.sql.catalog.local.type", "hadoop")
spark.conf.set("spark.sql.catalog.local.warehouse", "data/warehouse")
I have the data I need to use sitting in a directory /one/one.csv
How do I get it into an Iceberg table using Spark? Can all of this be done purely using SparkSQL?
spark.sql(
"""
CREATE or REPLACE TABLE local.db.one
USING iceberg
AS SELECT * FROM `/one/one.csv`
"""
)
Then the goal is I can work with this iceberg table directly for example:
select * from local.db.one
and this would give me all the content from the /one/one.csv file.

Answer 1

To use the SparkSQL, read the file into a dataframe, then register it as a temp view. This temp view can now be referred in the SQL as:
var df = spark.read.format("csv").load("/data/one.csv")
df.createOrReplaceTempView("tempview");
spark.sql("CREATE or REPLACE TABLE local.db.one USING iceberg AS SELECT * FROM tempview");
To answer your other question, Scala or Python is not required; the above example is in Java.

Answer 2

val sparkConf = new SparkConf()
sparkConf.set("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions")
sparkConf.set("spark.sql.catalog.spark_catalog", "org.apache.iceberg.spark.SparkSessionCatalog")
sparkConf.set("spark.sql.catalog.spark_catalog.type", "hive")
sparkConf.set("spark.sql.catalog.hive_catalog", "org.apache.iceberg.spark.SparkCatalog")
sparkConf.set("spark.sql.catalog.hive_catalog.type", "hadoop")
sparkConf.set("spark.sql.catalog.hive_catalog.warehouse", "hdfs://host:port/user/hive/warehouse")
sparkConf.set("hive.metastore.uris", "thrift://host:19083")
sparkConf.set("spark.sql.catalog.hive_prod", " org.apache.iceberg.spark.SparkCatalog")
sparkConf.set("spark.sql.catalog.hive_prod.type", "hive")
sparkConf.set("spark.sql.catalog.hive_prod.uri", "thrift://host:19083")
sparkConf.set("hive.metastore.warehouse.dir", "hdfs://host:port/user/hive/warehouse")
val spark: SparkSession = SparkSession.builder()
.enableHiveSupport()
.config(sparkConf)
.master("yarn")
.appName("kafkaTableTest")
.getOrCreate()
spark.sql(
"""
|
|create table if not exists hive_catalog.icebergdb.kafkatest1(
| company_id int,
| event string,
| event_time timestamp,
| position_id int,
| user_id int
|)using iceberg
|PARTITIONED BY (days(event_time))
|""".stripMargin)
import spark.implicits._
val df: DataFrame = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "kafka_server")
.option("subscribe", "topic")
.option("startingOffsets", "latest")
.load()
//.selectExpr("cast (value as string)")
val value: DataFrame = df.selectExpr("CAST(value AS STRING)")
.as[String]
.map(data => {
val json_str: JSONObject = JSON.parseObject(data)
val company_id: Integer = json_str.getInteger("company_id")
val event: String = json_str.getString("event")
val event_time: String = json_str.getString("event_time")
val position_id: Integer = json_str.getInteger("position_id")
val user_id: Integer = json_str.getInteger("user_id")
(company_id, event, event_time, position_id, user_id)
})
.toDF("company_id", "event", "event_time", "position_id", "user_id")
value.createOrReplaceTempView("table")
spark.sql(
"""
|select
| company_id,
| event,
| to_timestamp(event_time,'yyyy-MM-dd HH:mm:ss') as event_time,
| position_id,
| user_id
|from table
|""".stripMargin)
.writeStream
.format("iceberg")
.outputMode("append")
.trigger(Trigger.ProcessingTime(1, TimeUnit.MINUTES))
.option("path","hive_catalog.icebergdb.kafkatest1") // tablePath: catalog.db.tableName
.option("checkpointLocation","hdfspath")
.start()
.awaitTermination()
This example is reading data from Kafka and writing data to Iceberg table