Supported Spark Data Sources#
File Formats#
Parquet#
When spark.comet.scan.enabled is enabled, Parquet scans will be performed natively by Comet if all data types
in the schema are supported. When this option is not enabled, the scan will fall back to Spark. In this case,
enabling spark.comet.convert.parquet.enabled will immediately convert the data into Arrow format, allowing native
execution to happen after that, but the process may not be efficient.
Apache Iceberg#
Comet accelerates Iceberg scans of Parquet files. See the Iceberg Guide for more information.
CSV#
Comet does not provide native CSV scan, but when spark.comet.convert.csv.enabled is enabled, data is immediately
converted into Arrow format, allowing native execution to happen after that.
JSON#
Comet does not provide native JSON scan, but when spark.comet.convert.json.enabled is enabled, data is immediately
converted into Arrow format, allowing native execution to happen after that.
Data Catalogs#
Apache Iceberg#
See the dedicated Comet and Iceberg Guide.
Supported Storages#
Comet supports most standard storage systems, such as local file system and object storage.
HDFS#
Apache DataFusion Comet native reader seamlessly scans files from remote HDFS for supported formats
Using experimental native DataFusion reader#
Unlike to native Comet reader the Datafusion reader fully supports nested types processing. This reader is currently experimental only
To build Comet with native DataFusion reader and remote HDFS support it is required to have a JDK installed
Example:
Build a Comet for spark-3.5 provide a JDK path in JAVA_HOME
Provide the JRE linker path in RUSTFLAGS, the path can vary depending on the system. Typically JRE linker is a part of installed JDK
export JAVA_HOME="/opt/homebrew/opt/openjdk@11"
make release PROFILES="-Pspark-3.5" COMET_FEATURES=hdfs RUSTFLAGS="-L $JAVA_HOME/libexec/openjdk.jdk/Contents/Home/lib/server"
Start Comet with experimental reader and HDFS support as described and add additional parameters
--conf spark.comet.scan.impl=native_datafusion \
--conf spark.hadoop.fs.defaultFS="hdfs://namenode:9000" \
--conf spark.hadoop.dfs.client.use.datanode.hostname = true \
--conf dfs.client.use.datanode.hostname = true
Query a struct type from Remote HDFS
spark.read.parquet("hdfs://namenode:9000/user/data").show(false)
root
|-- id: integer (nullable = true)
|-- first_name: string (nullable = true)
|-- personal_info: struct (nullable = true)
| |-- firstName: string (nullable = true)
| |-- lastName: string (nullable = true)
| |-- ageInYears: integer (nullable = true)
25/01/30 16:50:43 INFO core/src/lib.rs: Comet native library version 0.12.0-SNAPSHOT initialized
== Physical Plan ==
* CometColumnarToRow (2)
+- CometNativeScan: (1)
(1) CometNativeScan:
Output [3]: [id#0, first_name#1, personal_info#4]
Arguments: [id#0, first_name#1, personal_info#4]
(2) CometColumnarToRow [codegen id : 1]
Input [3]: [id#0, first_name#1, personal_info#4]
25/01/30 16:50:44 INFO fs-hdfs-0.1.12/src/hdfs.rs: Connecting to Namenode (hdfs://namenode:9000)
+---+----------+-----------------+
|id |first_name|personal_info |
+---+----------+-----------------+
|2 |Jane |{Jane, Smith, 34}|
|1 |John |{John, Doe, 28} |
+---+----------+-----------------+
Verify the native scan type should be CometNativeScan.
More on HDFS Reader
Local HDFS development#
Configure local machine network. Add hostname to
/etc/hosts
127.0.0.1 localhost namenode datanode1 datanode2 datanode3
::1 localhost namenode datanode1 datanode2 datanode3
Start local HDFS cluster, 3 datanodes, namenode url is
namenode:9000
docker compose -f kube/local/hdfs-docker-compose.yml up
Check the local namenode is up and running on
http://localhost:9870/dfshealth.html#tab-overviewBuild a project with HDFS support
JAVA_HOME="/opt/homebrew/opt/openjdk@11" make release PROFILES="-Pspark-3.5" COMET_FEATURES=hdfs RUSTFLAGS="-L /opt/homebrew/opt/openjdk@11/libexec/openjdk.jdk/Contents/Home/lib/server"
Run local test
withSQLConf(
CometConf.COMET_ENABLED.key -> "true",
CometConf.COMET_EXEC_ENABLED.key -> "true",
CometConf.COMET_NATIVE_SCAN_IMPL.key -> CometConf.SCAN_NATIVE_DATAFUSION,
SQLConf.USE_V1_SOURCE_LIST.key -> "parquet",
"fs.defaultFS" -> "hdfs://namenode:9000",
"dfs.client.use.datanode.hostname" -> "true") {
val df = spark.read.parquet("/tmp/2")
df.show(false)
df.explain("extended")
}
}
Or use spark-shell with HDFS support as described above
S3#
Root CA Certificates#
One major difference between Spark and Comet is the mechanism for discovering Root
CA Certificates. Spark uses the JVM to read CA Certificates from the Java Trust Store, but native Comet
scans use system Root CA Certificates (typically stored
in /etc/ssl/certs on Linux). These scans will not be able to interact with S3 if the Root CA Certificates are not
installed.
Supported Credential Providers#
AWS credential providers can be configured using the fs.s3a.aws.credentials.provider configuration. The following table shows the supported credential providers and their configuration options:
Credential provider |
Description |
Supported Options |
|---|---|---|
|
Access S3 using access key and secret key |
|
|
Access S3 using temporary credentials |
|
|
Access S3 using AWS STS assume role |
|
|
Access S3 using EC2 instance profile or ECS task credentials (tries ECS first, then IMDS) |
None (auto-detected) |
|
Access S3 without authentication (public buckets only) |
None |
|
Load credentials from environment variables ( |
None |
|
Access S3 using EC2 instance metadata service (IMDS) |
None |
|
Access S3 using ECS task credentials |
None |
|
Authenticate using web identity token file |
None |
Multiple credential providers can be specified in a comma-separated list using the fs.s3a.aws.credentials.provider configuration, just as Hadoop AWS supports. If fs.s3a.aws.credentials.provider is not configured, Hadoop S3A’s default credential provider chain will be used. All configuration options also support bucket-specific overrides using the pattern fs.s3a.bucket.{bucket-name}.{option}.