Skip to main content

Spark -Teradata connection Issues


Exception: 

Caused by: java.lang.NullPointerException

        at com.teradata.tdgss.jtdgss.TdgssConfigApi.GetMechanisms(Unknown Source)

        at com.teradata.tdgss.jtdgss.TdgssManager.<init>(Unknown Source)

        at com.teradata.tdgss.jtdgss.TdgssManager.<clinit>(Unknown Source)

Brief:

tdgssconfig.jar can't be found on the classpath. Please add same on classpath.


Exception: 

java.sql.SQLException: [Teradata Database] [TeraJDBC 15.10.00.33] [Error 3707] [SQLState 42000] Syntax error, expected something like a name or a Unicode delimited identifier or an 'UDFCALLNAME' keyword or '(' between the 'FROM' keyword and the 'SELECT' keyword.

Brief:

Normally Spark JDBC expects DBTable property to be a Table Name. So, internally it prepends "select * from" to Table Name. Like 

select * from <Table Name>

But, If we specify SQL instead of  Table Name then internally SQL will become something like:

select * from select ... ;

Above makes it syntactically incorrect leading to above error. Solution is to provide subquery with an alias such that it will make internal SQL syntactically correct.

select * from (select ...) alias

Comments

Popular posts

Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...




Scala Spark building Jar leads java.lang.StackOverflowError

  Exception -  [Thread-3] ERROR scala_maven.ScalaCompileMojo - error: java.lang.StackOverflowError [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.generic.TraversableForwarder$class.isEmpty(TraversableForwarder.scala:36) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.isEmpty(ListBuffer.scala:45) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.toList(ListBuffer.scala:306) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.result(ListBuffer.scala:300) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.Stack$StackBuilder.result(Stack.scala:31) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.Stack$StackBuilder.result(Stack.scala:27) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.generic.GenericCompanion.apply(GenericCompanion.scala:50) [Thread-3] INFO scala_maven.ScalaCompile...




MongoDB Chunk size many times bigger than configure chunksize (128 MB)

  Shard Shard_0 at Shard_0/xyz.com:27018 { data: '202.04GiB', docs: 117037098, chunks: 5, 'estimated data per chunk': '40.4GiB', 'estimated docs per chunk': 23407419 } --- Shard Shard_1 at Shard_1/abc.com:27018 { data: '201.86GiB', docs: 116913342, chunks: 4, 'estimated data per chunk': '50.46GiB', 'estimated docs per chunk': 29228335 } Per MongoDB-  Starting in 6.0.3, we balance by data size instead of the number of chunks. So the 128MB is now only the size of data we migrate at-a-time. So large data size per chunk is good now, as long as the data size per shard is even for the collection. refer -  https://www.mongodb.com/community/forums/t/chunk-size-many-times-bigger-than-configure-chunksize-128-mb/212616 https://www.mongodb.com/docs/v6.0/release-notes/6.0/#std-label-release-notes-6.0-balancing-policy-changes




AWS EMR Spark – Much Larger Executors are Created than Requested

  Starting EMR 5.32 and EMR 6.2 you can notice that Spark can launch much larger executors that you request in your job settings. For example - We started a Spark Job with  spark.executor.cores  =   4 But, one can see that the executors with 20 cores (instead of 4 as defined by spark.executor.cores) were launched. The reason for allocating larger executors is that there is a AWS specific Spark option spark.yarn.heterogeneousExecutors.enabled (exists in EMR only, does not exist in Open Source Spark) that is set to true by default that combines multiple executor creation requests on the same node into a larger executor container. So as the result you have fewer executor containers than you expected, each of them has more memory and cores that you specified. If you disable this option (--conf "spark.yarn.heterogeneousExecutors.enabled=false"), EMR will create containers with the specified spark.executor.memory and spark.executor.cores settings and will not co...




Hive Parse JSON with Array Columns and Explode it in to Multiple rows.

 Say we have a JSON String like below -  { "billingCountry":"US" "orderItems":[       {          "itemId":1,          "product":"D1"       },   {          "itemId":2,          "product":"D2"       }    ] } And, our aim is to get output parsed like below -  itemId product 1 D1 2 D2   First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),...