I am trying to read data from ElasticSearch via Spark Scala. I see lot of post addressing this question, i have tried all the options they have mentioned in various posts but seems nothing is working for me
JAR Used - elasticsearch-hadoop-5.6.8.jar (Used elasticsearch-spark-5.6.8.jar too without any success)
Elastic Search Version - 5.6.8
Spark - 2.3.0
Scala - 2.11
Code:
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.elasticsearch.spark._
val spark = SparkSession.builder.appName("elasticSpark").master("local[*]").getOrCreate()
val reader = spark.read.format("org.elasticsearch.spark.sql").option("es.index.auto.create", "true").option("spark.serializer", "org.apache.spark.serializer.KryoSerializer").option("es.port", "9200").option("es.nodes", "xxxxxxxxx").option("es.nodes.wan.only", "true").option("es.net.http.auth.user","xxxxxx").option("es.net.http.auth.pass", "xxxxxxxx")
val read = reader.load("index/type")
Error:
ERROR rest.NetworkClient: Node [xxxxxxxxx:9200] failed (The server xxxxxxxxxxxxx failed to respond); no other nodes left - aborting...
org.elasticsearch.hadoop.EsHadoopIllegalArgumentException: Cannot detect ES version - typically this happens if the network/Elasticsearch cluster is not accessible or when targeting a WAN/Cloud instance without the proper setting 'es.nodes.wan.only'
at org.elasticsearch.hadoop.rest.InitializationUtils.discoverEsVersion(InitializationUtils.java:294)
at org.elasticsearch.spark.sql.SchemaUtils$.discoverMappingAndGeoFields(SchemaUtils.scala:98)
at org.elasticsearch.spark.sql.SchemaUtils$.discoverMapping(SchemaUtils.scala:91)
at org.elasticsearch.spark.sql.ElasticsearchRelation.lazySchema$lzycompute(DefaultSource.scala:129)
at org.elasticsearch.spark.sql.ElasticsearchRelation.lazySchema(DefaultSource.scala:129)
at org.elasticsearch.spark.sql.ElasticsearchRelation$$anonfun$schema$1.apply(DefaultSource.scala:133)
at org.elasticsearch.spark.sql.ElasticsearchRelation$$anonfun$schema$1.apply(DefaultSource.scala:133)
at scala.Option.getOrElse(Option.scala:121)
at org.elasticsearch.spark.sql.ElasticsearchRelation.schema(DefaultSource.scala:133)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:432)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:239)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:227)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:174)
... 53 elided
Caused by: org.elasticsearch.hadoop.rest.EsHadoopNoNodesLeftException: Connection error (check network and/or proxy settings)- all nodes failed; tried [[xxxxxxxxxxx:9200]]
at org.elasticsearch.hadoop.rest.NetworkClient.execute(NetworkClient.java:149)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:461)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:425)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:429)
at org.elasticsearch.hadoop.rest.RestClient.get(RestClient.java:155)
at org.elasticsearch.hadoop.rest.RestClient.remoteEsVersion(RestClient.java:655)
at org.elasticsearch.hadoop.rest.InitializationUtils.discoverEsVersion(InitializationUtils.java:287)
... 65 more
Apart from this I have also tried below properties without any success:
option("es.net.ssl.cert.allow.self.signed", "true")
option("es.net.ssl.truststore.location", "<path for elasticsearch cert file>")
option("es.net.ssl.truststore.pass", "xxxxxx")
Please note elasticsearch node is within Unix edge node and is http://xxxxxx:9200 (mentioning it just in case if that makes any difference with the code)
What I am missing here? Any other properties? Please Help
Use below Jar which support spark 2+ version instead of Elastic-Hadoop or Elastic-Spark jar.
https://mvnrepository.com/artifact/org.elasticsearch/elasticsearch-spark-20_2.11/5.6.8
Related
Using the steps documented in structured streaming pyspark, I'm unable to create a dataframe in pyspark from the Azure Event Hub I have set up in order to read the stream data.
Error message is:
java.util.ServiceConfigurationError: org.apache.spark.sql.sources.DataSourceRegister: Provider org.apache.spark.sql.eventhubs.EventHubsSourceProvider could not be instantiated
I have installed the Maven libraries (com.microsoft.azure:azure-eventhubs-spark_2.11:2.3.12 is unavailable) but none appear to work:
com.microsoft.azure:azure-eventhubs-spark_2.11:2.3.15
com.microsoft.azure:azure-eventhubs-spark_2.11:2.3.6
As well as ehConf['eventhubs.connectionString'] = sc._jvm.org.apache.spark.eventhubs.EventHubsUtils.encrypt(connectionString) but the error message returned is:
java.lang.NoSuchMethodError: org.apache.spark.internal.Logging.$init$(Lorg/apache/spark/internal/Logging;)V
The connection string is correct as it is also used in a console application that writes to the Azure Event Hub and that works.
Can someone point me in the right direction, please. Code in use is as follows:
from pyspark.sql.functions import *
from pyspark.sql.types import *
# Event Hub Namespace Name
NAMESPACE_NAME = "*myEventHub*"
KEY_NAME = "*MyPolicyName*"
KEY_VALUE = "*MySharedAccessKey*"
# The connection string to your Event Hubs Namespace
connectionString = "Endpoint=sb://{0}.servicebus.windows.net/;SharedAccessKeyName={1};SharedAccessKey={2};EntityPath=ingestion".format(NAMESPACE_NAME, KEY_NAME, KEY_VALUE)
ehConf = {}
ehConf['eventhubs.connectionString'] = connectionString
# For 2.3.15 version and above, the configuration dictionary requires that connection string be encrypted.
# ehConf['eventhubs.connectionString'] = sc._jvm.org.apache.spark.eventhubs.EventHubsUtils.encrypt(connectionString)
df = spark \
.readStream \
.format("eventhubs") \
.options(**ehConf) \
.load()
To resolve the issue, I did the following:
Uninstall azure event hub library versions
Install com.microsoft.azure:azure-eventhubs-spark_2.11:2.3.15 library version from Maven Central
Restart cluster
Validate by re-running code provided in the question
I received this same error when installing libraries with the version number com.microsoft.azure:azure-eventhubs-spark_2.11:2.3.* on a Spark cluster running Spark 3.0 with Scala 2.12
For anyone else finding this via google - check if you have the correct Scala library version. In my case, my cluster is Spark v3 with Scala 2.12
Changing the "2.11" in the library version from the tutorial I was using to "2.12", so it matches my cluster runtime version, fixed the issue.
I had to take this a step further. in the format method I had to add in this:
.format("org.apache.spark.sql.eventhubs.EventHubsSourceProvider") directly.
check the cluster scala version and the library version
Unisntall the older libraries and install :
com.microsoft.azure:azure-eventhubs-spark_2.12:2.3.17
in the shared workspace(right click and install library) and also in the cluster
I want to send messages from a Twitter application to an Azure event hub. However, I am getting an error that says:
notebook:20: error: type mismatch;
found : java.util.concurrent.ExecutorService
required: java.util.concurrent.ScheduledExecutorService
val eventHubClient = EventHubClient.create(connStr.toString(), pool)
I do not know how to create the EventHubClient.create now. Please help.
I am referring to code from the link
https://learn.microsoft.com/en-us/azure/azure-databricks/databricks-stream-from-eventhubs.
Also, I have tried the solution from link:
Stream data into Azure Databricks using Event Hubs and it doesn't work for me.
The version of the cluster is 5.2 (includes Apache Spark 2.4.0, Scala 2.11) which should include the Java SE 8 libraries that have the new ScheduledExecutorService member. Also, the libraries attached are com.microsoft.azure:azure-eventhubs-spark_2.11:2.3.9 and org.twitter4j:twitter4j-core:4.0.7, so again all the prerequisites are met.
The code is:
import java._
import java.util._
import scala.collection.JavaConverters._
import com.microsoft.azure.eventhubs._
import java.util.concurrent._
import java.util.concurrent.ExecutorService
import java.util.concurrent.ScheduledExecutorService
val pool = Executors.newFixedThreadPool(1)
val eventHubClient = EventHubClient.create(connStr.toString(), pool)
I've read up everything I could on SO and the ReactiveMongo community list and I am stumped. I am using ReactiveMongo version 0.12 and am just trying to test it out since I have some other problems.
The code in my scala worksheet is:
import reactivemongo.api.{DefaultDB, MongoConnection, MongoDriver}
import reactivemongo.bson.{
BSONDocumentWriter, BSONDocumentReader, Macros, document
}
import com.typesafe.config.{Config, ConfigFactory}
lazy val conf = ConfigFactory.load()
val driver1 = new reactivemongo.api.MongoDriver
val connection3 = driver1.connection(List("localhost"))
and the error I get is
[NGSession 3: 127.0.0.1: compile-server] INFO reactivemongo.api.MongoDriver - No mongo-async-driver configuration found
com.typesafe.config.ConfigException$Missing: No configuration setting found for key 'akka'
at com.typesafe.config.impl.SimpleConfig.findKey(testMongo.sc:120)
at com.typesafe.config.impl.SimpleConfig.find(testMongo.sc:143)
at com.typesafe.config.impl.SimpleConfig.find(testMongo.sc:155)
at com.typesafe.config.impl.SimpleConfig.find(testMongo.sc:160)
at com.typesafe.config.impl.SimpleConfig.getString(testMongo.sc:202)
at akka.actor.ActorSystem$Settings.<init>(testMongo.sc:165)
at akka.actor.ActorSystemImpl.<init>(testMongo.sc:501)
at akka.actor.ActorSystem$.apply(testMongo.sc:138)
at reactivemongo.api.MongoDriver.<init>(testMongo.sc:879)
at #worksheet#.driver1$lzycompute(testMongo.sc:9)
at #worksheet#.driver1(testMongo.sc:9)
at #worksheet#.get$$instance$$driver1(testMongo.sc:9)
at #worksheet#.#worksheet#(testMongo.sc:30)
My application.conf is in src/main/resources of the sub-project which this worksheet is found and contains this:
mongo-async-driver {
akka {
loglevel = WARNING
}
}
I added the ConfigFactory precisely because I got this error and thought it might help. I looked at the code and that's what ReactiveMongo is doing at this point so I thought perhaps a call here would force it to load at this point. I have moved the application.conf file into every conceivable place including a conf directory (thinking it might require play conventions) and the src/main/resources of the top level directory. Nothing works. So my first question is what am I doing wrong? Where should application.conf file go?
This info message causes my program to crash and driver doesn't get created so I can't move on from here.
Also, I added an akka key to reference.conf just in case - that didnt help either.
Using sbt package I have the following error
Spark Scala error while loading BytesWritable, invalid LOC header (bad signature)
My code is
....
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf
......
object Test{
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("Test")
val sc = new SparkContext(conf) // the error is due by this
......
}
}
Pls re-load your JARs and / or library dependencies as they might be corrupted while building jar through sbt - could be issue with one of their update. Second alternative is that you have too many temp files open, check your 4040-9 ports on master if there are any jobs hanging and kill them if so, you can also check how increase open files you have on linux:/etc/security/limits.conf where hard nofile ***** and soft nofile ***** then reboot and ulimit -n ****
I was using spark-mllib_2.11 and it gave me the same error. I had to use version 2.10 of Spark MLIB to get rid of it.
Using Maven:
<artifactId>spark-mllib_2.10</artifactId>
I have asked this question previously also but did not got any answer (Not able to connect to postgres using jdbc in pyspark shell).
I have successfully installed Spark 1.3.0 on my local windows and ran sample programs to test using pyspark shell.
Now, I want to run Correlations from Mllib on the data that is stored in Postgresql, but I am not able to connect to postgresql.
I have successfully added the required jar (tested this jar) in the classpath by running
pyspark --jars "C:\path\to\jar\postgresql-9.2-1002.jdbc3.jar"
I can see that jar is successfully added in environment UI.
When I run the following in pyspark shell-
from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
df = sqlContext.load(source="jdbc",url="jdbc:postgresql://[host]/[dbname]", dbtable="[schema.table]")
I get this ERROR -
>>> df = sqlContext.load(source="jdbc",url="jdbc:postgresql://[host]/[dbname]", dbtable="[schema.table]")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\ACERNEW3\Desktop\Spark\spark-1.3.0-bin-hadoop2.4\python\pyspark\sql\context.py", line 482, in load
df = self._ssql_ctx.load(source, joptions)
File "C:\Users\ACERNEW3\Desktop\Spark\spark-1.3.0-bin-hadoop2.4\python\lib\py4j-0.8.2.1-src.zip\py4j\java_gateway.py", line 538, in __call__
File "C:\Users\ACERNEW3\Desktop\Spark\spark-1.3.0-bin-hadoop2.4\python\lib\py4j-0.8.2.1-src.zip\py4j\protocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o20.load.
: java.sql.SQLException: No suitable driver found for jdbc:postgresql://[host]/[dbname]
at java.sql.DriverManager.getConnection(DriverManager.java:602)
at java.sql.DriverManager.getConnection(DriverManager.java:207)
at org.apache.spark.sql.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:94)
at org.apache.spark.sql.jdbc.JDBCRelation.<init> (JDBCRelation.scala:125)
at org.apache.spark.sql.jdbc.DefaultSource.createRelation(JDBCRelation.scala:114)
at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:290)
at org.apache.spark.sql.SQLContext.load(SQLContext.scala:679)
at org.apache.spark.sql.SQLContext.load(SQLContext.scala:667)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
at java.lang.reflect.Method.invoke(Method.java:597)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:619)
I had this exact problem with mysql/mariadb, and got BIG clue from this question
So your pyspark command should be:
pyspark --conf spark.executor.extraClassPath=<jdbc.jar> --driver-class-path <jdbc.jar> --jars <jdbc.jar> --master <master-URL>
Also watch for errors when pyspark start like "Warning: Local jar ... does not exist, skipping." and "ERROR SparkContext: Jar not found at ...", these probably mean you spelled the path wrong.
A slightly more elegant solution:
val props = new Properties
props.put("driver", "org.postgresql.Driver")
sqlContext.read.jdbc("jdbc:postgresql://[host]/[dbname]", props)
As jake256 suggested
"driver", "org.postgresql.Driver"
key-value pair was missing. In my case, I launched pyspark as :
pyspark --jars /path/to/postgresql-9.4.1210.jar
with following instructions :
from pyspark.sql import DataFrameReader
url = 'postgresql://192.168.2.4:5432/postgres'
properties = {'user': 'myUser', 'password': 'myPasswd', 'driver': 'org.postgresql.Driver'}
df = DataFrameReader(sqlContext).jdbc(
url='jdbc:%s' % url, table='weather', properties=properties
)
df.show()
+-------------+-------+-------+-----------+----------+
| city|temp_lo|temp_hi| prcp| date|
+-------------+-------+-------+-----------+----------+
|San Francisco| 46| 50| 0.25|1994-11-27|
|San Francisco| 43| 57| 0.0|1994-11-29|
| Hayward| 54| 37|0.239999995|1994-11-29|
+-------------+-------+-------+-----------+----------+
Tested on :
Ubuntu 16.04
PostgreSQL server version 9.5.
Postgresql driver used is postgresql-9.4.1210.jar
and Spark version is spark-2.0.0-bin-hadoop2.6
but I am also confident that it should also work on
spark-2.0.0-bin-hadoop2.7.
Java JDK 1.8 64bits
other JDBC Drivers can be found on :
https://www.petefreitag.com/articles/jdbc_urls/
tutorial I followed is on :
https://developer.ibm.com/clouddataservices/2015/08/19/speed-your-sql-queries-with-spark-sql/
similar solution was suggested also on :
pyspark mysql jdbc load An error occurred while calling o23.load No suitable driver
This error seems to get thrown when you use the wrong version of JDBC driver. Check https://jdbc.postgresql.org/download.html to make sure that you have the right one.
Note in particular:
JDK 1.1 - JDBC 1. Note that with the 8.0 release JDBC 1 support has
been removed, so look to update your JDK when you update your server.
JDK 1.2, 1.3 - JDBC 2. JDK 1.3 + J2EE - JDBC 2 EE. This contains
additional support for javax.sql classes.
JDK 1.4, 1.5 - JDBC 3. This contains support for SSL and javax.sql, but does not require J2EE as it has been added to the J2SE release. JDK 1.6 - JDBC4. Support for JDBC4 methods is not complete, but the majority of methods are implemented.
JDK 1.7, 1.8 - JDBC41. Support for JDBC4 methods is not
complete, but the majority of methods are implemented.
see this post please, just place your script after all the options. see this
That’s pretty straightforward. To connect to external database to retrieve data into Spark dataframes additional jar file is required. E.g. with MySQL the JDBC driver is required. Download the driver package and extract mysql-connector-java-x.yy.zz-bin.jar in a path that’s accessible from every node in the cluster. Preferably this is a path on shared file system. E.g. with Pouta Virtual Cluster such path would be under /shared_data, here I use /shared_data/thirdparty_jars/.
With direct Spark job submissions from terminal one can specify –driver-class-path argument pointing to extra jars that should be provided to workers with the job. However this does not work with this approach, so we must configure these paths for front end and worker nodes in the spark-defaults.conf file, usually in /opt/spark/conf directory.
spark.driver.extraClassPath /"your-path"/mysql-connector-java-5.1.35-bin.jar
spark.executor.extraClassPath /"your-path"/mysql-connector-java-5.1.35-bin.jar