I am trying to read (and eventually write) from azurite (version 3.18.0) using spark (3.1.1)
i can't understand what spark configurations and file uri i need to set to make this work properly
for example these are the containers and files i have inside azurite
/devstoreaccount1/container1/file1.avro
/devstoreaccount1/container2/file2.avro
This is the code that im running - the uri val is one of the values below
val uri = ...
val spark = SparkSession.builder()
.appName(appName)
.master("local")
.config("spark.driver.host", "127.0.0.1").getOrCreate()
spark.conf.set("spark.hadoop.fs.wasbs.impl", "org.apache.hadoop.fs.azure.NativeAzureFileSystem")
spark.conf.set(s"spark.hadoop.fs.azure.account.auth.type.devstoreaccount1.blob.core.windows.net", "SharedKey")
spark.conf.set(s"spark.hadoop.fs.azure.account.key.devstoreaccount1.blob.core.windows.net", <azurite account key>)
spark.read.format("avro").load(uri)
uri value - what is the correct one?
http://127.0.0.1:10000/container1/file1.avro
I get UnsupportedOperationException when i perform the spark.read.format("avro").load(uri) because spark will use the HttpFileSystem implementation and it doesn't support listStatus
wasb://container1#devstoreaccount1.blob.core.windows.net/file1.avro
Spark will try to authenticate against azure servers (and will fail for obvious reasons)
I have tried to follow this stackoverflow post without success.
I have also tried to remove the blob.core.windows.net configuration postfix but then i don't how to give spark the endpoint for the azurite container?
So my question is what are the correct configurations to give spark so it will be able to read from azurite, and what are the correct file path formats to pass as the URI?
Related
I am new with NiFi, I am trying to send data from NiFi to Spark or to establish a stream from NiFi output port to Spark according to this tutorial.
Nifi is running on Kubernetes and I am using Spark operator on the same cluster to submit my applications.
It seems like Spark is able to access the web NiFi and it starts a streaming receiver. However, data is not coming to the Spark app through output and I have empty rdds. I have not seen any warnings or errors in Spark logs
Any Idea or information which could help me to solve this issue is appreciated.
My code:
val conf = new SiteToSiteClient.Builder()
.keystoreFilename("..")
.keystorePass("...")
.keystoreType(...)
.truststoreFilename("..")
.truststorePass("..")
.truststoreType(...)
.url("https://...../nifi")
.portName("spark")
.buildConfig()
val lines = ssc.receiverStream(new NiFiReceiver(conf, StorageLevel.MEMORY_ONLY))
I want my Spark app (Scala) to be able to read S3 files
spark.read.parquet("s3://my-bucket-name/my-object-key")
On my dev machine, I could access S3 files using awscli a pre-configured profile in ~/.aws/config or ~/.aws/credentials, like:
aws --profile my-profile s3 ls s3://my-bucket-name/my-object-key
But when trying to read those files from Spark, with the aws_profile provided as an env variable (AWS_PROFILE), I got the following error:
doesBucketExist on my-bucket-name: com.amazonaws.AmazonClientException: No AWS Credentials provided by BasicAWSCredentialsProvider EnvironmentVariableCredentialsProvider SharedInstanceProfileCredentialsProvider : com.amazonaws.SdkClientException: Unable to load credentials from service endpoint
Also tried to provide the profile as a JVM option (-Daws.profile=my-profile), with no luck.
Thanks for reading.
The solution is to provide the spark property: fs.s3a.aws.credentials.provider, setting it to com.amazonaws.auth.profile.ProfileCredentialsProvider.
If I could change the code to build the Spark Session, then something like:
SparkSession
.builder()
.config("fs.s3a.aws.credentials.provider","com.amazonaws.auth.profile.ProfileCredentialsProvider")
.getOrCreate()
The other way is to provide the JVM option -Dspark.hadoop.fs.s3a.aws.credentials.provider=com.amazonaws.auth.profile.ProfileCredentialsProvider.*NOTE the prefix spark.hadoop
If problems arise still after setting fs.s3a.aws.credentials.provider to com.amazonaws.auth.profile.ProfileCredentialsProvider and correctly setting AWS_PROFILE, it might be because you're using Hadoop 2 for which the above configuration is not supported.
Therefore, the only workaround I found was to upgrade to Hadoop 3.
Check this post and Hadoop docs for more information.
I am trying to run a simple Apache spark (Cloudera) read operation using a local object store that is fully s3 sdk/api compatible. But I can not seem to figure out how to get Spark to understand that I am trying to access a local S3 bucket and not remote AWS/S3.
Here's what I've tried...
pyspark2 --conf spark.hadoop.hadoop.security.credential.provider.path=jceks://hdfs/user/myusername/awskeyfile.jceks --conf fs.s3a.endpoint=https://myenvironment.domain.com
df = spark.read.parquet("s3a://mybucket/path1/")
Error message...
Caused by: com.amazonaws.SdkClientException: Unable to execute HTTP request: Connect to mybucket.s3.amazonaws.com:443 [mybucket.s3.amazonaws.com/12.345.678.90] failed: Connection refused (Connection refused)
I can list the local bucket contents without issue on the command-line so I know that I have the access/secret key correct but I need to make Spark understand not to reach out to aws to try and resolve the bucket url.
Thanks.
Update / Resolution:
The fix to the issue was a missing prerequisite jar at maven coordinates: org.apache.hadoop:hadoop-aws:2.6.0
So the final pyspark call looked like:
pyspark2 --conf spark.hadoop.hadoop.security.credential.provider.path=jceks://hdfs/user/myusername/awskeyfile.jceks --conf fs.s3a.endpoint=https://myenvironment.domain.com --jars hadoop-aws-2.6.0.jar
df = spark.read.parquet("s3a://mybucket/path1/")
This is covered in HDP docs, Working with third party object stores.
Settings are the same for CDH.
It comes down
endpoint fs.s3a.endpoint = hostname
disable DNS to bucket map fs.s3a.path.style.access = true
play with signing options.
There are a few other switches you can turn for better compatibility; they're in those docs.
You might find the Cloudstore storediag command useful.
I'm trying to configure SSL for the Cassandra Spark connector, but I couldn't find an example of how to do it.
I'm trying to configure it like this:
SparkConf conf = new SparkConf().setAppName("someApp")
.set("spark.cassandra.connection.host", "111.111.111.111")
.set("spark.cassandra.connection.ssl.enabled", "true")
.set("spark.cassandra.connection.ssl.trustStore.path", "/some/tfile.jks")
.set("spark.cassandra.connection.ssl.trustStore.password", "apassword")
.set("spark.cassandra.connection.ssl.trustStore.type", "JKS")
.set("spark.cassandra.connection.ssl.enabledAlgorithms", "TLS_RSA_WITH_AES_128_CBC_SHA,TLS_RSA_WITH_AES_256_CBC_SHA")
.set("spark.cassandra.connection.ssl.keyStore.path", "/some/kfile.jks")
.set("spark.cassandra.connection.ssl.keyStore.password", "anotherpassword")
.set("spark.cassandra.connection.ssl.keyStore.type", "JKS")
.set("spark.cassandra.connection.ssl.protocol", "TLS");
When I try to submit the spark job, I get these errors:
Exception in thread "main" com.datastax.spark.connector.util.ConfigCheck$ConnectorConfigurationException: Invalid Config Variables
Only known spark.cassandra.* variables are allowed when using the Spark Cassandra Connector.
spark.cassandra.connection.ssl.keyStore.password is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.enabled is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.protocol is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.keyStore.type is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.trustStore.path is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.enabledAlgorithms is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.keyStore.path is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.trustStore.password is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.trustStore.type is not a valid Spark Cassandra Connector variable.
No likely matches found.
So I'm not sure if this is supported or I'm just using the wrong property names.
I saw this ticket for release 1.2.3 of the connector, but I couldn't find an example of how to use it and it sounded like it may not support keystores. I'm using version 1.4.0-M1 of the connector.
Can anyone show me an example of how to configure SSL for the Spark Cassandra connector? Thanks.
Though I don't see any keystore configurations, I can see below config variables and they are working fine for me.
Note: I am using 1.5.0-M1 version. Not sure if there is any other bug in the version you are using.
sparkConf.set("spark.cassandra.connection.ssl.enabled", "true");
sparkConf.set("spark.cassandra.connection.ssl.trustStore.password", "password");
sparkConf.set("spark.cassandra.connection.ssl.trustStore.path", "jks file path");
We have processed our data using Spark 1.2.1 with Java and stored in Hive tables. We want to access this data as RDDs from an web browser.
I read documentation and I understood the steps to do the task.
I am unable to find the way to interact with Spark SQL RDDs via thrift server. Examples I found have belw line in the code and I am not find the class for this in Spark 1.2.1 java API docs.
HiveThriftServer2.startWithContext
In github i saw scala examples using
import org.apache.spark.sql.hive.thriftserver , but I dont see this in Java API docs. Not sure if I am missing something.
Did anybody had luck with accessing Spark SQL RDDs from a browser via thrift? Can you post the code snippet. We are using Java.
I've got most of this working. Lets dissect each part of it: (References at bottom of post)
HiveThriftServer2.startWithContext is defined in Scala. I was never able to access it from Java or from Python using Py4j, and am no JVM expert, but I ended up switching to Scala. This may have something to do with the annotation #DeveloperApi . This is how I imported it Scala in Spark 1.6.1:
import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2
For anyone reading this and not using Hive, a Spark SQL context won't do, and you need a hive context. However, the HiveContext constructor requires a Java spark context, not a scala one.
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.hive.HiveContext
var hiveContext = new HiveContext(JavaSparkContext.toSparkContext(sc))
Now start the thrift server
HiveThriftServer2.startWithContext(hiveContext)
// Yay
Next, we need to make our RDDs available as SQL tables. First, we have to convert them into Spark SQL DataFrames:
val someDF = hiveContext.createDataFrame(someRDD)
Then, we need to turn them into Spark SQL tables. You do this by persisting them to Hive, or making the RDD available as a temporary table.
Persist to Hive:
// Deprecated since Spark 1.4, to be removed in Spark 2.0:
someDF.saveAsTable("someTable")
// Up-to-date at time of writing
someDF.write().saveAsTable("someTable")
Or, use a temporary table:
// Use the Data Frame as a Temporary Table
// Introduced in Spark 1.3.0
someDF.registerTempTable("someTable")
Note - temporary tables are isolated to an SQL session.
Spark's hive thrift server is multi-session by default
in version 1.6 (one session per connection). Therefore,
for clients to access temporary tables you've registered,
you'll need to set the option spark.sql.hive.thriftServer.singleSession to true
You can test this by querying the tables in beeline, a command line utility for interacting with the hive thrift server. It ships with Spark.
Finally, you need a way of accessing the hive thrift server from the browser. Thanks to its awesome developers, it has an HTTP mode, so if you want to build a web app, you can use the thrift protocol over AJAX requests from the browser. A simpler strategy might be to create an IPython notebook, and use pyhive to connect to the thrift server.
Data Frame Reference:
https://spark.apache.org/docs/1.6.0/api/java/org/apache/spark/sql/DataFrame.html
singleSession option pull request:
https://mail-archives.apache.org/mod_mbox/spark-commits/201511.mbox/%3Cc2bd1313f7ca4e618ec89badbd8f9f31#git.apache.org%3E
HTTP mode and beeline howto:
https://spark.apache.org/docs/latest/sql-programming-guide.html#distributed-sql-engine
Pyhive:
https://github.com/dropbox/PyHive
HiveThriftServer2 startWithContext definition:
https://github.com/apache/spark/blob/6b1a6180e7bd45b0a0ec47de9f7c7956543f4dfa/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala#L56-73
Thrift is JDBC/ODBC server.
You can connect to it via JDBC/ODBC connections and access content through the HiveDriver.
You can not get RDDs back from it, because HiveContext is not available.
What you refered to is an experimental feature not available for Java.
As a workaround, you could re-parse the results and create your structures for your client.
For example:
private static String driverName = "org.apache.hive.jdbc.HiveDriver";
private static String hiveConnectionString = "jdbc:hive2://YourHiveServer:Port";
private static String tableName = "SOME_TABLE";
Class c = Class.forName(driverName);
Connection con = DriverManager.getConnection(hiveConnectionString, "user", "pwd");
Statement stmt = con.createStatement();
String sql = "select * from "+tableName;
ResultSet res = stmt.executeQuery(sql);
parseResultsToObjects(res);