Hadoop 2.6 doesn't support s3a out of the box, so I've tried a series of solutions and fixes, including:
deploy with hadoop-aws and aws-java-sdk => cannot read environment variable for credentials add hadoop-aws into maven => various transitive dependency conflicts
Has anyone successfully make both work?
Having experienced first hand the difference between s3a and s3n - 7.9GB of data transferred on s3a was around ~7 minutes while 7.9GB of data on s3n took 73 minutes [us-east-1 to us-west-1 unfortunately in both cases; Redshift and Lambda being us-east-1 at this time] this is a very important piece of the stack to get correct and it's worth the frustration.
Here are the key parts, as of December 2015:
Your Spark cluster will need a Hadoop version 2.x or greater. If you use the Spark EC2 setup scripts and maybe missed it, the switch for using something other than 1.0 is to specify --hadoop-major-version 2 (which uses CDH 4.2 as of this writing).
You'll need to include what may at first seem to be an out of date AWS SDK library (built in 2014 as version 1.7.4) for versions of Hadoop as late as 2.7.1 (stable): aws-java-sdk 1.7.4. As far as I can tell using this along with the specific AWS SDK JARs for 1.10.8 hasn't broken anything.
You'll also need the hadoop-aws 2.7.1 JAR on the classpath. This JAR contains the class org.apache.hadoop.fs.s3a.S3AFileSystem.
In spark.properties you probably want some settings that look like this:
spark.hadoop.fs.s3a.access.key=ACCESSKEY spark.hadoop.fs.s3a.secret.key=SECRETKEY
If you are using hadoop 2.7 version with spark then the aws client uses V2 as default auth signature. And all the new aws region support only V4 protocol. To use V4 pass these conf in spark-submit and also endpoint (format - s3.<region>.amazonaws.com) must be specified.
--conf "spark.executor.extraJavaOptions=-Dcom.amazonaws.services.s3.enableV4=true
--conf "spark.driver.extraJavaOptions=-Dcom.amazonaws.services.s3.enableV4=true
I've detailed this list in more detail on a post I wrote as I worked my way through this process. In addition I've covered all the exception cases I hit along the way and what I believe to be the cause of each and how to fix them.
I'm writing this answer to access files with S3A from Spark 2.0.1 on Hadoop 2.7.3
Copy the AWS jars(hadoop-aws-2.7.3.jar and aws-java-sdk-1.7.4.jar) which shipped with Hadoop by default
Hint: If the jar locations are unsure? Running find command as a privileged user can be helpful; commands can be
find / -name hadoop-aws*.jar
find / -name aws-java-sdk*.jar
into spark classpath which holds all spark jars
Hint: We can not directly point the location(It must be in property file) as I want to make an answer generic for distributions and Linux flavors. spark classpath can be identified by find command below
find / -name spark-core*.jar
spark-defaults.confHint: (Mostly it will be placed in /etc/spark/conf/spark-defaults.conf)
#make sure jars are added to CLASSPATH
spark.yarn.jars=file://{spark/home/dir}/jars/*.jar,file://{hadoop/install/dir}/share/hadoop/tools/lib/*.jar
spark.hadoop.fs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem
spark.hadoop.fs.s3a.access.key={s3a.access.key}
spark.hadoop.fs.s3a.secret.key={s3a.secret.key}
#you can set above 3 properties in hadoop level `core-site.xml` as well by removing spark prefix.
in spark submit include jars(aws-java-sdk and hadoop-aws) in --driver-class-path if needed.
spark-submit --master yarn \
--driver-class-path {spark/jars/home/dir}/aws-java-sdk-1.7.4.jar \
--driver-class-path {spark/jars/home/dir}/hadoop-aws-2.7.3.jar \
other options
Note:
Make sure the Linux user with reading privileges, before running the
findcommand to prevent error Permission denied
I got it working using the Spark 1.4.1 prebuilt binary with hadoop 2.6
Make sure you set both spark.driver.extraClassPath and spark.executor.extraClassPath pointing to the two jars (hadoop-aws and aws-java-sdk)
If you run on a cluster, make sure your executors have access to the jar files on the cluster.
We're using spark 1.6.1 with Mesos and we were getting lots of issues writing to S3 from spark. I give credit to cfeduke for the answer. The slight change I made was adding maven coordinates to the spark.jar config in the spark-defaults.conf file. I tried with hadoop-aws:2.7.2 but was still getting lots of errors so we went back to 2.7.1. Below are the changes in spark-defaults.conf that are working for us:
spark.jars.packages net.java.dev.jets3t:jets3t:0.9.0,com.google.guava:guava:16.0.1,com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.1
spark.hadoop.fs.s3a.access.key <MY ACCESS KEY>
spark.hadoop.fs.s3a.secret.key <MY SECRET KEY>
spark.hadoop.fs.s3a.fast.upload true
Thank you cfeduke for taking the time to write up your post. It was very helpful.
Here are the details as of October 2016, as presented at Spark Summit EU: Apache Spark and Object Stores.
Key points
Product placement: the read-performance side of HADOOP-11694 is included in HDP2.5; The Spark and S3 documentation there might be of interest —especially the tuning options.
as you said, hadoop 2.6 doesn't support s3a, and latest spark release 1.6.1 doesn't support hadoop 2.7, but spark 2.0 is definitely no problem with hadoop 2.7 and s3a.
for spark 1.6.x, we made some dirty hack, with the s3 driver from EMR... you can take a look this doc: https://github.com/zalando/spark-appliance#emrfs-support
if you still want to try to use s3a in spark 1.6.x, refer to the answer here: https://stackoverflow.com/a/37487407/5630352
Using Spark 1.4.1 pre-built with Hadoop 2.6, I am able to get s3a:// to work when deploying to a Spark Standalone cluster by adding the hadoop-aws and aws-java-sdk jar files from the Hadoop 2.7.1 distro (found under $HADOOP_HOME/share/hadoop/tools/lib of Hadoop 2.7.1) to my SPARK_CLASSPATH environment variable in my $SPARK_HOME/conf/spark-env.sh file.
You can also add the S3A dependencies to the classpath using spark-defaults.conf.
Example:
spark.driver.extraClassPath /usr/local/spark/jars/hadoop-aws-2.7.5.jar
spark.executor.extraClassPath /usr/local/spark/jars/hadoop-aws-2.7.5.jar
spark.driver.extraClassPath /usr/local/spark/jars/aws-java-sdk-1.7.4.jar
spark.executor.extraClassPath /usr/local/spark/jars/aws-java-sdk-1.7.4.jar
Or just:
spark.jars /usr/local/spark/jars/hadoop-aws-2.7.5.jar,/usr/local/spark/jars/aws-java-sdk-1.7.4.jar
Just make sure to match your AWS SDK version to the version of Hadoop. For more information about this, look at this answer: Unable to access S3 data using Spark 2.2
Here's a solution for pyspark (possibly with proxy):
def _configure_s3_protocol(spark, proxy=props["proxy"]["host"], port=props["proxy"]["port"], endpoint=props["s3endpoint"]["irland"]):
"""
Configure access to the protocol s3
https://sparkour.urizone.net/recipes/using-s3/
AWS Regions and Endpoints
https://docs.aws.amazon.com/general/latest/gr/rande.html
"""
sc = spark.sparkContext
sc._jsc.hadoopConfiguration().set("fs.s3a.access.key", os.environ.get("AWS_ACCESS_KEY_ID"))
sc._jsc.hadoopConfiguration().set("fs.s3a.secret.key", os.environ.get("AWS_SECRET_ACCESS_KEY"))
sc._jsc.hadoopConfiguration().set("fs.s3a.proxy.host", proxy)
sc._jsc.hadoopConfiguration().set("fs.s3a.proxy.port", port)
sc._jsc.hadoopConfiguration().set("fs.s3a.endpoint", endpoint)
return spark
Here is a scala version that works fine with Spark 3.2.1 (pre-built) with Hadoop 3.3.1, accessing a S3 bucket from a non AWS machine [typically a local setup on a developer machine]
sbt
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % "3.2.1" % "provided",
"org.apache.spark" %% "spark-streaming" % "3.2.1" % "provided",
"org.apache.spark" %% "spark-sql" % "3.2.1" % "provided",
"org.apache.hadoop" % "hadoop-aws" % "3.3.1",
"org.apache.hadoop" % "hadoop-common" % "3.3.1" % "provided"
)
spark program
val spark = SparkSession
.builder()
.master("local")
.appName("Process parquet file")
.config("spark.hadoop.fs.s3a.path.style.access", true)
.config("spark.hadoop.fs.s3a.access.key", ACCESS_KEY)
.config("spark.hadoop.fs.s3a.secret.key", SECRET_KEY)
.config("spark.hadoop.fs.s3a.endpoint", ENDPOINT)
.config(
"spark.hadoop.fs.s3a.impl",
"org.apache.hadoop.fs.s3a.S3AFileSystem"
)
// The enable V4 does not seem necessary for the eu-west-3 region
// see @stevel comment below
// .config("com.amazonaws.services.s3.enableV4", true)
// .config(
// "spark.driver.extraJavaOptions",
// "-Dcom.amazonaws.services.s3.enableV4=true"
// )
.config("spark.executor.instances", "4")
.getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
val df = spark.read.parquet("s3a://[BUCKET NAME]/.../???.parquet")
df.show()
note: region is in the form s3.[REGION].amazonaws.com e.g. s3.eu-west-3.amazonaws.com
s3 configuration
To make the bucket available from outside of AWS, add a Bucket Policy of the form:
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "Statement1",
"Effect": "Allow",
"Principal": {
"AWS": "arn:aws:iam::[ACCOUNT ID]:user/[IAM USERNAME]"
},
"Action": [
"s3:Delete*",
"s3:Get*",
"s3:List*",
"s3:PutObject"
],
"Resource": "arn:aws:s3:::[BUCKET NAME]/*"
}
]
}
The supplied ACCESS_KEY and SECRET_KEY to the spark configuration must be those of the IAM user configured on the bucket
I am using spark version 2.3, and when I save a dataset using spark like:
dataset.write().format("hive").option("fileFormat", "orc").mode(SaveMode.Overwrite)
.option("path", "s3://reporting/default/temp/job_application")
.saveAsTable("job_application");
It works perfectly and saves my data into s3.
