Skip to main content



spark-sftp com.springml.spark.sftp org.apache.spark.sql.AnalysisException: Path does not exist:

 

We had been facing issue with using spark-sftp Jar while downloading a remote SFTP file and creating Dataframe.

Code being executed:

val df = spark.read.format("com.springml.spark.sftp")
.option("host", HOST)
.option("port", PORT)
.option("username", UN)
.option("password", PWD)
.option("fileType", "csv")
.option("inferSchema", "true")
.option("header", "true")
.load(FILENAME)

Error response:

org.apache.spark.sql.AnalysisException: Path does not exist: wasb://...

at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:612)

at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:595)

at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)

at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)

at scala.collection.immutable.List.foreach(List.scala:381)

at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)

at scala.collection.immutable.List.flatMap(List.scala:344)

at org.apache.spark.sql.execution.datasources.DataSource.org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary(DataSource.scala:595)

at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:390)

Analysis:

We found that SFTP is file downloading into local file location as specified by "yarn.nodemanager.local-dirs" when running in Cluster mode, whereas file is being downloaded in local as specified by "spark.local.dir" when running in Client mode.


But, problem is that file is being searched over HDFS/ WASB instead of Local to create Dataframe which results in "Path not exists" exception

It is pointed out here that issue has been fixed in latest version of Jar file - https://github.com/springml/spark-sftp/issues/52. But, we were still getting same exception.


Solution:

We uploaded all the Jar files to HDFS / WASB and pointed same with --jars instead of local file system. Our code worked just fine after placing Jar files on HDFS and using same instead of local file system.

Command when using local file system resulting error -

spark-submit --jars /home/sshuser/ds/sftp.client-1.0.3.jar,/home/sshuser/ds/spark-sftp_2.11-1.1.5.jar,/home/sshuser/ds/spark-sql-kafka-0-10_2.11-2.3.0.jar


Command when using HDFS/ WASB, running successfully -

spark-submit --jars wasb:///home/sshuser/ds/sftp.client-1.0.3.jar,wasb:///home/sshuser/ds/spark-sftp_2.11-1.1.5.jar,wasb:///home/sshuser/ds/spark-sql-kafka-0-10_2.11-2.3.0.jar

Comments

Post a Comment

Popular posts

Unix Server ( Edge Node ) hangs when there are many jobs running on hadoop cluster started from Unix Edge Node.

  When a unix server or an edge node is running lots of jobs (like Spark, Hadoop, or custom batch processes), crashes happen. For example. For example a process might hit a segementation fault, memory issue or ay other runtime issue. By default, if ulimit -c is not 0, the OS will create core dump. Core dump are written to disk and can be very large, sometimes hundreds of MBs or even GBs per process. What we realized was that when multiple processes crash at the same time, the system suddenly tries to write core files to disk. This was leading to DisK I/O spikes. Thus, node was becoming unresponsive. This was also leading CPU spike because OS was handling crash logging. Setting "ulimit -c 0" disables core dumps. This way we lose ability to debug crashes via core dump But, kept production edge nodes stable. On most Linux systems, by default, "core dumps" are written in current working directory of the process that crashes. Linux allows you to change core dump file nam...
Post a Comment
Read more




VPN testing checklist

 To test: Is VPN active? Is it leaking? Does it look residential? Does it alter network fingerprints? Public IP Test (Basic Detection) - curl ipinfo.io DNS Leak Test - nslookup google.com MTU Test (Encapsulation Detection) - ping 8.8.8.8 -f -l 1472 TCP MSS Inspection - Use Wireshark and Look for SYN packets. Traceroute Path Test - tracert 8.8.8.8 WebRTC Leak Test - browserleaks.com/webrtc IPv6 Leak Test - test-ipv6.com Latency / Jitter Test - ping -n 50 8.8.8.8. VPN usually causes: More Jitters and Latency. Reverse DNS (PTR Record) - nslookup <your-public-ip> IP Reputation Check - Like most IP's from VPN Providers like Express VPN, etc. are blacklisted and all. Look for: Proxy/VPN classification, Hosting provider tagging
Post a Comment
Read more




Spark-JDBC connection with Oracle Fails - java.sql.SQLSyntaxErrorException: ORA-00903: invalid table name

  While connecting Spark with Oracle JDBC, one may observe exception like below -  spark.read.format("jdbc"). option("url", "jdbc:oracle:thin:@//oraclehost:1521/servicename"). option("dbtable", "mytable"). option("user", "myuser").option("driver", "oracle.jdbc.driver.OracleDriver") option("password", "mypassword"). load().write.parquet("/data/out") java.sql.SQLSyntaxErrorException: ORA-00903: invalid table name at oracle.jdbc.driver.T4CTTIoer.processError(T4CTTIoer.java:447) at oracle.jdbc.driver.T4CTTIoer.processError(T4CTTIoer.java:396) at oracle.jdbc.driver.T4C8Oall.processError(T4C8Oall.java:951) at oracle.jdbc.driver.T4CTTIfun.receive(T4CTTIfun.java:513) at oracle.jdbc.driver.T4CTTIfun.doRPC(T4CTTIfun.java:227) at oracle.jdbc.driver.T4C8Oall.doOALL(T4C8Oall.java:531) at oracle.jdbc.driver.T4CPreparedStatement.doOall8(T4CPreparedStatement.java:208) ...
Post a Comment
Read more




Spring MongoDB Rest API not returning response in 90 seconds which is leading to client timeout

  We have Spring Boot  Rest API deployed in Kubernetes cluster which integrates with MongoDB to fetch the data.  MongoDB is fed with data by a real time Spark & NiFi job.  Our clients complained that for a request what they send they don't have response within 90 seconds. Consider it like an OMS ( Order ManagEment System).  On further analysis, we found that Spark & NiFi processing is happenning within 10 seconds after consuming response data from Kafka. Thus, initally out thought was that it due to delay from upstream to produce data in to Kafka.  Thankfully, our data had create / request  timestamp, and when response was received, and when response was inserted into MongoDB. Subtracting response insert time from request time seemed to be well within 90 seconds. But, still client did timeout on not seeing a response within 90 seconds. This led to confusion on our side.  But, then we realized it was due to Read Preference . We updated this...
Post a Comment
Read more




MongoDB Regex Query taking more time in Production but same query perform well in UAT

   We came across a situation where-in, MongoDB Query was taking more time in Production like 10 seconds and 4.2 seconds but same query performed well in UAT taking under 400 ms. The very first thought that was evident to us that it is because of amount of data which differed in UAT and Production. Then we ran following to see the execution plan -   db.collection.aggregate(<queries>).explain() This gave us Winning and Rejected Plans. Under which, we analyzed that although it was using 'IXSCAN.' But, it was incorrect index- as we had one compound index built on time field and other fields, and there was other index just on time field for TTL purposes. Winning plan picked TTL index rather than compound index. Thus, we dropped TTL index and built TTL index on a different time field.  That got our query performance time from 10 seconds to 726 ms. Also, for other query the performance came down from 8 seconds to 4.3 seconds. Then, we ran following -  ...
Post a Comment
Read more