I use pyspark with spark 2.2.0 on a lubuntu 16.04 and I want to write a Dataframe to my Postgresql database. Now as far as I understand it I have to install a jdbc driver on the spark master for it. I downloaded the postgresql jdbc driver from their website and tried to follow this post. I added spark.jars.packages /path/to/driver/postgresql-42.2.1.jar to spark-default.conf with the only result that pyspark no longer launches.
I'm kinda lost in java land for one I don't know if this is the right format.The documentation tells me I should add a list but I don't know how a path list is supposed to look like. Then I don't know if I also have to specify spark.jars and or spark.driver.extraClassPath or if spark.jars.packages is enough? And if i have to add them what kind of format are they?
spark.jars.packages is for dependencies that can be pulled from Maven (think it as pip for Java, although the analogy is probably kinda loose).
You can submit your job with the option --jars /path/to/driver/postgresql-42.2.1.jar, so that the submission will also provide the library, that the cluster manager will distribute on all worker nodes on your behalf.
If you want to set this as a configuration you can use the spark.jars key instead of spark.jars.packages. The latter requires Maven coordinates, rather then a path (which is probably the reason why your job is failing).
You can read more about the configuration keys I introduced on the official documentation.
Related
I want to read data from cassandra node in my client node on :
This is what i tried :
spark-shell --jars /my-dir/spark-cassandra-connector_2.11-2.3.2.jar.
val df = spark.read.format("org.apache.spark.sql.cassandra")\
.option("keyspace","my_keyspace")\
.option("table","my_table")\
.option("spark.cassandra.connection.host","Hostname of my Cassandra node")\
.option("spark.cassandra.connection.port","9042")\
.option("spark.cassandra.auth.password","mypassword)\
.option("spark.cassandra.auth.username","myusername")\
.load
I'm getting this error: java.lang.NoClassDefFoundError: Could not initialize class org.apache.spark.sql.cassandra.DefaultSource$
and
java.lang.NoClassDefFoundError: org/apache/commons/configuration/ConfigurationException.
Am I missing any properties? What this error is for ? How would I resolve this ?
Spark-version:2.3.2, DSE version 6.7.8
The Spark Cassandra Connector itself depends on the number of other dependencies, that could be missing here - this happens because you're providing only one jar, and not all required dependencies.
Basically, in your case you need to have following choice:
If you're running this on the DSE node, then you can use built-in Spark, if the cluster has Analytics enabled - in this case, all jars and properties are already provided, and you only need to provide username and password when starting spark shell via dse -u user -p password spark
if you're using external Spark, then it's better to use so-called BYOS (bring your own spark) - special version of the Spark Cassandra Connector with all dependencies bundled inside, and you can download jar from DataStax's Maven repo, and use with --jars
you can still use open source Spark Cassandra Connector, but in this case, it's better to use --packages com.datastax.spark:spark-cassandra-connector_2.11:2.3.2 so Spark will able to fetch all dependencies automatically.
P.S. In case of open source Spark Cassandra Connector I would recommend to use version 2.5.1 or higher, although it requires Spark 2.4.x (although 2.3.x may work) - this version has improved support for DSE, plus a lot of the new functionality not available in the earlier versions. Plus for that version there is a version that includes all required dependencies (so-called assembly) that you can use with --jars if your machine doesn't have access to the internet.
I am trying to run existing Spark (Scala) code on AWS Glue.
This code uses spark.read.option("jdbc") and I have been adding the JDBC driver to the Spark classpath with the spark.driver.extraClassPath option.
This works fine locally as well as on EMR, assuming I can copy the driver from S3 to the instances first with a bootstrap action.
But what's the equivalent on Glue? If I add the driver to the "dependent JARs" option, it doesn't work and I get the "no suitable driver" error, presumably because the JAR must be visible to Spark's own classloader.
Edit your job and at the end of the screen, you can see the libraries option.
And some options are needed, see the last part of the documentation.
I get a lot of data uploaded to an S3 bucket that I want so analyze/visualize using Spark and Zeppelin. Yet, I am still stuck at loading data from S3.
I did some reading in order to get this together and spare me gory details. I am using the docker container p7hb/docker-spark as Spark installation and my basic test for reading data from S3 is derived from here:
I start the container and a master and a slave process within. I can validate this works by looking at the Spark Master WebUI, exposed on port 8080. This page does list the worker and keeps a log of all my failed attempts under the headline "Completed Applications". All of those are in the state FINISHED.
I open a bash inside that container and do the following:
a) export the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, as suggested here.
b) start spark-shell. In order to access S3 one seems to need to load some extra packages. Browsing through SE I found especially this, which teaches me, that I can use the --packages parameter to load said packages. Essentially I run spark-shell --packages com.amazonaws:aws-java-sdk:1.7.15,org.apache.hadoop:hadoop-aws:2.7.5(, for arbitrary combinations of versions).
c) I run the following code
sc.hadoopConfiguration.set("fs.s3a.endpoint", "s3-eu-central-1.amazonaws.com")
sc.hadoopConfiguration.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
sc.hadoopConfiguration.set("com.amazonaws.services.s3.enableV4", "true")
val sonnets=sc.textFile("s3a://my-bucket/my.file")
val counts = sonnets.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
And then I get all kinds of different Error messages, depending on the versions I choose in 2b).
I suppose there is nothing wrong with 2a), b/c I get the error message Unable to load AWS credentials from any provider in the chain if I don't supply those. This is a known error new users seem to make.
While trying to solve the issue, I pick more or less random versions from here and there for the two extra packages. Somewhere on SE I read that hadoop-aws:2.7 is supposed to be the right choice, because Spark 2.2 is based on Hadoop 2.7. Supposedly one needs to use aws-java-sdk:1.7 with that version of hadoop-aws.
Whatever! I tried thefollowing combinations
--packages com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.1, which yields the common Bad Request 400 error.
Many problems can lead to that error, my attempt as described above containseverything I was able to find on this page. The description above contains s3-eu-central-1.amazonaws.com as endpoint, while other places use s3.eu-central-1.amazonaws.com. According to enter link description here, both endpoint names are supposed to work. I did try both.
--packages com.amazonaws:aws-java-sdk:1.7.15,org.apache.hadoop:hadoop-aws:2.7.5, which are the most recent micro versions in either case, I get the error message java.lang.NoSuchMethodError: com.amazonaws.services.s3.transfer.TransferManager.<init>(Lcom/amazonaws/services/s3/AmazonS3;Ljava/util/concurrent/ThreadPoolExecuto
r;)V
--packages com.amazonaws:aws-java-sdk:1.11.275,org.apache.hadoop:hadoop-aws:2.7.5, I also get java.lang.NoSuchMethodError: com.amazonaws.services.s3.transfer.TransferManager.<init>(Lcom/amazonaws/services/s3/AmazonS3;Ljava/util/concurrent/ThreadPoolExecutor;)V
--packages com.amazonaws:aws-java-sdk:1.11.275,org.apache.hadoop:hadoop-aws:2.8.1, I get java.lang.IllegalAccessError: tried to access method org.apache.hadoop.metrics2.lib.MutableCounterLong.<init>(Lorg/apache/hadoop/metrics2/MetricsInfo;J)V from class org.apache.hadoop.fs.s3a.S3AInstrumentation
--packages com.amazonaws:aws-java-sdk:1.11.275,org.apache.hadoop:hadoop-aws:2.8.3, I also get java.lang.IllegalAccessError: tried to access method org.apache.hadoop.metrics2.lib.MutableCounterLong.<init>(Lorg/apache/hadoop/metrics2/MetricsInfo;J)V from class org.apache.hadoop.fs.s3a.S3AInstrumentation
--packages com.amazonaws:aws-java-sdk:1.8.12,org.apache.hadoop:hadoop-aws:2.8.3, I also get java.lang.IllegalAccessError: tried to access method org.apache.hadoop.metrics2.lib.MutableCounterLong.<init>(Lorg/apache/hadoop/metrics2/MetricsInfo;J)V from class org.apache.hadoop.fs.s3a.S3AInstrumentation
--packages com.amazonaws:aws-java-sdk:1.11.275,org.apache.hadoop:hadoop-aws:2.9.0, I also get java.lang.NoClassDefFoundError: org/apache/hadoop/fs/StorageStatistics
And, for completeness sake, when I don't provide the --packages parameter, I get java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found.
Currently nothing seems to work. Yet, there are so many Q/As on this topic, who knows what's the way du jour of doing this. This is all in local mode, so there is virtually no other source of error. My method of accessing S3 must be wrong. How is it done correctly?
Edit 1:
So I put another day into this, without any actual progress. As far as I can tell, starting from Hadoop 2.6, Hadoop doesn't have built in support for S3 anymore, but it as to be loaded through additional libraries, which are not part of Hadoop and entirely managed by itself. Besides all the clutter, the library I ultimately want seems to be hadoop-aws. It has a webpage here andit carries what I would call authoritative information:
The versions of hadoop-common and hadoop-aws must be identical.
The important thing about this information is, that hadoop-common actually does ship with a Hadoop installation. Every Hadoop installation has a corresponding jar file, so this is a solid starting point. My containers have a file /usr/hadoop-2.7.3/share/hadoop/common/hadoop-common-2.7.3.jar so it is fair to assume 2.7.3 is the version I need for hadoop-aws.
After that it gets murky. Hadoop versions 2.7.x have something going on internally, so that they are not compatible with more recent versions of aws-java-sdk, which is a library required by hadoop-aws. The Internet is full of advice to use version 1.7.4, for example here, but other comments suggest to using version 1.7.14 for 2.7.x.
So I did another run using hadoop-aws:2.7.3 and aws-java-sdk:1.7.x, with x ranging from 4 to 14. No results whatsover, I always end up with error 400, Bad Request.
My Hadoop installation ships joda-time 2.9.4. I read the problem was resolved with Hadoop 2.8. I suppose I will just go ahead and build my own docker containers with more recent versions.
Edit 2
Moved to Hadoop 2.8.3. It just works now. Turns out you don't even have to mess around with JARs at all. Hadoop ships with what are supposed to be working JARs for accessing AWS S3. They are hidden in ${HADOOP_HOME}/share/hadoop/tools/lib and not added to the classpath by default. I simply load the JARS in that directory, execute my code as stated above and now it works.
Mixing and matching AWS SDK JARs with anything else is an exercise in futility, as you've discovered. You need the version of the AWS JARs Hadoop was built with, and the version of Jackson AWS was built with. Oh, and don't try mixing any of (different amazon-* JARs, different hadoop-* JARs, different jackson-* JARs); they all go in lock-sync.
For Spark 2.2.0 and Hadoop 2.7, use AWS 1.7.4 artifacts, and make sure that if you are on Java 8, that Joda time is > 2.8.0, such as 2.9.4. That can lead to 400 "bad auth problems".
Otherwise, try Troubleshooting S3A
I am using pyspark in an ipython notebook and accessing a netezza database. I am trying to do something similar on bluemix. The problem is that in order to have access to netezza, I have to add parameters to the pyspark startup. How can I do that on bluemix? Here is how I start pyspark standalone:
$ PYSPARK_DRIVER_PYTHON=ipython PYSPARK_DRIVER_PYTHON_OPTS="notebook" /usr/local/src/spark/spark-1.6.1-bin-hadoop2.6/bin/pyspark --packages com.ibm.SparkTC:spark-netezza_2.10:0.1.1 --driver-class-path /usr/local/src/netezza/jdbc/lib/nzjdbc3.jar
You cannot change the parameters for starting PySpark on Bluemix.
The %AddJar kernel magic works for the Scala notebooks, only.
It does not work for Python notebooks.
The driver for Netezza nzjdbc3.jar has to be provided and supported on the service in order to make us of it. Currently, this cannot be done by the user.
Update:
nzjdbc3.jar is not supported out of box. You could submit feedback via E-Mail and ask for the driver to be supported.
Another possibility to enable the driver for PySpark is to put the jar into a location which will be considered for PySpark configuration.
First, find out your USER_ID by using the following command:
!whoami
Then, get nzjdbc3.jar and put it to the following location:
/gpfs/fs01/user/USER_ID/data/libs
One way to put nzjdbc3.jar into the mentioned location is to use wget:
!wget URI_TO_JAR_FILE -P /gpfs/fs01/user/USER_ID/data/libs
After the driver jar was downloaded to the mentioned location, you have to restart the kernel. During the creation of the new kernel all files in the mentioned location will be considered for PySpark.
I want to submit spark python applications from my laptop. I have a standalone spark cluster, and the master is running at some visible IP (MASTER_IP). After downloading and unzipping Spark on my laptop, I got this to work
./bin/spark-submit --master spark://MASTER_IP:7077 ~/PATHTO/pi.py
From what I understand, it is defaulting to client mode (vs cluster mode). According to Spark (http://spark.apache.org/docs/latest/submitting-applications.html) -
"only YARN supports cluster mode for Python applications." Since I'm not using YARN, I must use client mode.
My question is - do I need to download all of Spark on my laptop? Or just a few libraries?
I want to allow the rest of my team to use my Spark cluster, but I want them to do the least amount of work as possible. They don't need to setup a cluster. They only need to submit jobs to it. Having them downloading all of Spark seems like overkill.
So, what exactly is the minimum that they need?
The spark-1.5.0-bin-hadoop2.6 package I have here is 304MB unpacked. More than half, 175MB is made up of spark-assembly-1.5.0-hadoop2.6.0.jar, the main Spark stuff. You can't get rid of this unless you want to compile your own package maybe. A large part of the rest is spark-examples-1.5.0-hadoop2.6.0.jar, 113MB. Removing this and zipping back up is harmless and saves you a lot already.
However, using some tools such that they don't have to work with the spark package directly, like spark-jobserver (never used but never heard somebody very positive about the current state) or spark-kernel (needs your own code still to interface with it, or when used with notebook (see below) limited compared to alternatives) as suggested by Reactormonk makes it even easier for them.
A popular thing to do in that sense is set up access to a notebook. As you're using Python, IPython with a PySpark profile would be most straightforward to set up. Other alternatives are Zeppelin and spark-notebook (my favourite) for using Scala.