I'm trying to use glue streaming and write data to AWS TimestreamDB but I'm having a hard time in configuring the JDBC connection.
Steps I’m following are below and the documentation link: https://docs.aws.amazon.com/timestream/latest/developerguide/JDBC.configuring.html
I’m uploading the jar to S3. There are multiple jars here and I tried with each one of it. https://github.com/awslabs/amazon-timestream-driver-jdbc/releases
In the glue job I’m pointing the jar lib path to the above s3 location
In the job script I’m trying to read from timestream using both spark/ glue with the below code but its not working. Can someone explain what I'm doing wrong here
This is my code:
url = jdbc:timestream://AccessKeyId=<myAccessKeyId>;SecretAccessKey=<mySecretAccessKey>;SessionToken=<mySessionToken>;Region=us-east-1
source_df = sparkSession.read.format("jdbc").option("url",url).option("dbtable","IoT").option("driver","software.amazon.timestream.jdbc.TimestreamDriver").load()
datasink1 = glueContext.write_dynamic_frame.from_options(frame = applymapping0, connection_type = "jdbc", connection_options = {"url":url,"driver":"software.amazon.timestream.jdbc.TimestreamDriver", database = "CovidTestDb", dbtable = "CovidTestTable"}, transformation_ctx = "datasink1")
To this date (April 2022) there is not support for write operations using timestream's jdbc driver (reviewed the code and saw a bunch of no write support exceptions). It is possible to read data from timestream using glue though. Following steps worked for me:
Upload timestream-query and timestream-jdbc to an S3 bucket that you can reference in your glue script
Ensure that the IAM role for the script has access to read operations to the timestream database and table
You don't need to use the access key and secret parameters in the jdbc url, using something like jdbc:timestream://Region=<timestream-db-region> should be enough
Specify the driver and fetchsize options option("driver","software.amazon.timestream.jdbc.TimestreamDriver")
option("fetchsize", "100") (tweak the fetchsize according to your needs)
Following is a complete example of reading a dataframe from timestream:
val df = sparkSession.read.format("jdbc")
.option("url", "jdbc:timestream://Region=us-east-1")
.option("driver","software.amazon.timestream.jdbc.TimestreamDriver")
// optionally add a query to narrow the data to fetch
.option("query", "select * from db.tbl where time between ago(15m) and now()")
.option("fetchsize", "100")
.load()
df.write.format("console").save()
Hope this helps
We are building a streaming platform where it is essential to work with SQL's in batches.
val query = streamingDataSet.writeStream.option("checkpointLocation", checkPointLocation).foreachBatch { (df, batchId) => {
df.createOrReplaceTempView("events")
val df1 = ExecutionContext.getSparkSession.sql("select * from events")
df1.limit(5).show()
// More complex processing on dataframes
}}.trigger(trigger).outputMode(outputMode).start()
query.awaitTermination()
Error thrown is :
org.apache.spark.sql.streaming.StreamingQueryException: Table or view not found: events
Caused by: org.apache.spark.sql.catalyst.analysis.NoSuchTableException: Table or view 'events' not found in database 'default';
Streaming source is Kafka with watermarking and without using Spark-SQL we are able to execute dataframe transformations. Spark version is 2.4.0 and Scala is 2.11.7. Trigger is ProcessingTime every 1 minute and OutputMode is Append.
Is there any other approach to facilitate use of spark-sql within foreachBatch ? Would it work with upgraded version of Spark - in which case to version do we upgrade ?
Kindly help. Thank you.
tl;dr Replace ExecutionContext.getSparkSession with df.sparkSession.
The reason of the StreamingQueryException is that the streaming query tries to access the events temporary table in a SparkSession that knows nothing about it, i.e. ExecutionContext.getSparkSession.
The only SparkSession that has this events temporary table registered is exactly the SparkSession the df dataframe is created within, i.e. df.sparkSession.
Please check the code snippet below. Here, I have created two separate DataFrames, responseDF1 and responseDF2 from resultDF and shown the output in the console. responseDF2 is created using a temporary table. You can try the same.
resultDF.writeStream.foreachBatch {(batchDF: DataFrame, batchId: Long) =>
batchDF.persist()
val responseDF1 = batchDF.selectExpr("ResponseObj.type","ResponseObj.key", "ResponseObj.activity", "ResponseObj.price")
responseDF1.show()
responseDF1.createTempView("responseTbl1")
val responseDF2 = batchDF.sparkSession.sql("select activity, key from responseTbl1")
responseDF2.show()
batchDF.sparkSession.catalog.dropTempView("responseTbl1")
batchDF.unpersist()
()}.start().awaitTermination()
Code Snippet
I'm trying to consume Avro records from Kafka using Nifi. I have 3 topics filled from an Amazon Lambda and 2 Spark Streaming jobs, all of which use HortonWorks Schema Registry to get the Avro schema.
I tried to use a ConsumeKafkaRecord_0_10 and ConsumeKafkaRecord_2_0 and getting the same error:
I tried with an AvroReader using plain text schema inside, to be sure of the one being used, and got the same error.
When I'm using an AvroReader with the Horton Schema Registry parameter I'm getting this error:
Which could make sens because it's looking at the first byte of the record as a version parameter for the schema, and the first byte is 3. But it doesn't explain why I'm getting ArrayIndexOutOfBound when putting the schema in plain text.
Finally I can consume those topic just fine using Spark Streaming and Schema Registry. Doesn't anyone already encounter such an issue between NiFi and AvroReader when consuming Kafka.
Stack: Horton Works HDP 3.4.1 // Nifi 1.9.0 // Spark 2.3 // Schema Registry 0.7
The issue is related to how Nifi is interpreting first bytes of your Avro message. Those bytes contain information regaring:
Protocol Id - 1 byte
Schema Metadata Id - 8 bytes
Schema Version - 4 bytes
Going through the code of HortonWork Schema Registry we can find that different Protocol ID can be used to serialize your message with the AvroSerDe.
public static final byte CONFLUENT_VERSION_PROTOCOL = 0x0;
public static final byte METADATA_ID_VERSION_PROTOCOL = 0x1;
public static final byte VERSION_ID_AS_LONG_PROTOCOL = 0x2;
public static final byte VERSION_ID_AS_INT_PROTOCOL = 0x3;
public static final byte CURRENT_PROTOCOL = VERSION_ID_AS_INT_PROTOCOL;
Source
The default one used is VERSION_ID_AS_INT_PROTOCOL which means the first byte of the Avro messages is going to be 03.
When going through Nifi code, we see that it's actually using METADATA_ID_VERSION_PROTOCOL only, expecting a 01 and not taking into account anything else.
You have to force Spark to use METADATA_ID_VERSION_PROTOCOL when creating you SchemaRegistryConfig.
val config = Map[String, Object](
"schema.registry.url" -> ConfigManager.config.getProperty("schemaregistry.default.url"),
AbstractAvroSnapshotSerializer.SERDES_PROTOCOL_VERSION -> SerDesProtocolHandlerRegistry.METADATA_ID_VERSION_PROTOCOL.asInstanceOf[Object]
)
implicit val srConfig:SchemaRegistryConfig = SchemaRegistryConfig(config)
I am trying to integrate Cassandra with Spark and facing the below issue.
Issue:
com.datastax.spark.connector.util.ConfigCheck$ConnectorConfigurationException: Invalid Config Variables
Only known spark.cassandra.* variables are allowed when using the Spark Cassandra Connector.
spark.cassandra.keyspace is not a valid Spark Cassandra Connector variable.
Possible matches:
spark.cassandra.sql.keyspace
spark.cassandra.output.batch.grouping.key
at com.datastax.spark.connector.util.ConfigCheck$.checkConfig(ConfigCheck.scala:50)
at com.datastax.spark.connector.cql.CassandraConnectorConf$.apply(CassandraConnectorConf.scala:253)
at org.apache.spark.sql.cassandra.CassandraSourceRelation$.apply(CassandraSourceRelation.scala:263)
at org.apache.spark.sql.cassandra.CassandraCatalog.org$apache$spark$sql$cassandra$CassandraCatalog$$buildRelation(CasandraCatalog.scala:41)
at org.apache.spark.sql.cassandra.CassandraCatalog$$anon$1.load(CassandraCatalog.scala:26)
at org.apache.spark.sql.cassandra.CassandraCatalog$$anon$1.load(CassandraCatalog.scala:23)
Please find the below versions of spark Cassandra and connector I am using.
Spark : 1.6.0
Cassandra : 2.1.17
Connector Used : spark-cassandra-connector_2.10-1.6.0-M1.jar
Below is the code snippet I am using to connect Cassandra from spark.
val conf: org.apache.spark.SparkConf = new SparkConf(true) \
.setAppName("Spark Cassandra") \
.set"spark.cassandra.connection.host", "abc.efg.lkh") \
.set("spark.cassandra.auth.username", "xyz") \
.set("spark.cassandra.auth.password", "1234") \
.set("spark.cassandra.keyspace","abcded")
val sc = new SparkContext("local[*]", "Spark Cassandra",conf)
val csc = new CassandraSQLContext(sc)
csc.setKeyspace("abcded")
val my_df = csc.sql("select * from table")
Here when I try to create DF, I am getting above posted error. I tried without passing schema in conf but it is trying to access in default schema where mentioned user doesn't have access.
Already a JIRA was opened and closed.
https://datastax-oss.atlassian.net/browse/SPARKC-102
yet I am getting this issue. Please let me know whether I need to use lastest connector to resolve this issue.
Thanks in advance.
The important information is in the error message you posted [formatted for readability]:
Invalid Config Variables
Only known spark.cassandra.* variables are allowed when using the Spark Cassandra Connector.
spark.cassandra.keyspace is not a valid Spark Cassandra Connector variable.
Possible matches: spark.cassandra.sql.keyspace
spark.cassandra.keyspace is not an available property for the connector. A full list of the available properties can be found here: https://github.com/datastax/spark-cassandra-connector/blob/master/doc/reference.md
You may have some luck using the suggested spark.cassandra.sql.keyspace; otherwise you may just need to explicitly specify the keyspace for every Cassandra interaction you perform using the connector.
I'm trying to get the path to spark.worker.dir for the current sparkcontext.
If I explicitly set it as a config param, I can read it back out of SparkConf, but is there anyway to access the complete config (including all defaults) using PySpark?
Spark 2.1+
spark.sparkContext.getConf().getAll() where spark is your sparksession (gives you a dict with all configured settings)
Yes: sc.getConf().getAll()
Which uses the method:
SparkConf.getAll()
as accessed by
SparkContext.sc.getConf()
See it in action:
In [4]: sc.getConf().getAll()
Out[4]:
[(u'spark.master', u'local'),
(u'spark.rdd.compress', u'True'),
(u'spark.serializer.objectStreamReset', u'100'),
(u'spark.app.name', u'PySparkShell')]
update configuration in Spark 2.3.1
To change the default spark configurations you can follow these steps:
Import the required classes
from pyspark.conf import SparkConf
from pyspark.sql import SparkSession
Get the default configurations
spark.sparkContext._conf.getAll()
Update the default configurations
conf = spark.sparkContext._conf.setAll([('spark.executor.memory', '4g'), ('spark.app.name', 'Spark Updated Conf'), ('spark.executor.cores', '4'), ('spark.cores.max', '4'), ('spark.driver.memory','4g')])
Stop the current Spark Session
spark.sparkContext.stop()
Create a Spark Session
spark = SparkSession.builder.config(conf=conf).getOrCreate()
Spark 1.6+
sc.getConf.getAll.foreach(println)
For a complete overview of your Spark environment and configuration I found the following code snippets useful:
SparkContext:
for item in sorted(sc._conf.getAll()): print(item)
Hadoop Configuration:
hadoopConf = {}
iterator = sc._jsc.hadoopConfiguration().iterator()
while iterator.hasNext():
prop = iterator.next()
hadoopConf[prop.getKey()] = prop.getValue()
for item in sorted(hadoopConf.items()): print(item)
Environment variables:
import os
for item in sorted(os.environ.items()): print(item)
Simply running
sc.getConf().getAll()
should give you a list with all settings.
Unfortunately, no, the Spark platform as of version 2.3.1 does not provide any way to programmatically access the value of every property at run time. It provides several methods to access the values of properties that were explicitly set through a configuration file (like spark-defaults.conf), set through the SparkConf object when you created the session, or set through the command line when you submitted the job, but none of these methods will show the default value for a property that was not explicitly set. For completeness, the best options are:
The Spark application’s web UI, usually at http://<driver>:4040, has an “Environment” tab with a property value table.
The SparkContext keeps a hidden reference to its configuration in PySpark, and the configuration provides a getAll method: spark.sparkContext._conf.getAll().
Spark SQL provides the SET command that will return a table of property values: spark.sql("SET").toPandas(). You can also use SET -v to include a column with the property’s description.
(These three methods all return the same data on my cluster.)
For Spark 2+ you can also use when using scala
spark.conf.getAll; //spark as spark session
You can use:
sc.sparkContext.getConf.getAll
For example, I often have the following at the top of my Spark programs:
logger.info(sc.sparkContext.getConf.getAll.mkString("\n"))
Just for the records the analogous java version:
Tuple2<String, String> sc[] = sparkConf.getAll();
for (int i = 0; i < sc.length; i++) {
System.out.println(sc[i]);
}
Suppose I want to increase the driver memory in runtime using Spark Session:
s2 = SparkSession.builder.config("spark.driver.memory", "29g").getOrCreate()
Now I want to view the updated settings:
s2.conf.get("spark.driver.memory")
To get all the settings, you can make use of spark.sparkContext._conf.getAll()
Hope this helps
Not sure if you can get all the default settings easily, but specifically for the worker dir, it's quite straigt-forward:
from pyspark import SparkFiles
print SparkFiles.getRootDirectory()
If you want to see the configuration in data bricks use the below command
spark.sparkContext._conf.getAll()
I would suggest you try the method below in order to get the current spark context settings.
SparkConf.getAll()
as accessed by
SparkContext.sc._conf
Get the default configurations specifically for Spark 2.1+
spark.sparkContext.getConf().getAll()
Stop the current Spark Session
spark.sparkContext.stop()
Create a Spark Session
spark = SparkSession.builder.config(conf=conf).getOrCreate()