When an application or job that typically completes in a short time is taking significantly longer than expected, it's essential to systematically troubleshoot the issue to identify and resolve the bottleneck. Here are some steps and areas to focus on when diagnosing performance issues in such scenarios: 1. Understand the Baseline and Gather Information Historical Performance Data: Compare the current run with previous runs. Identify what has changed in terms of input size, configuration, environment, etc. Logs and Metrics: Gather logs and metrics from the application, YARN ResourceManager, and NodeManager. 2. Monitor Resource Utilization CPU, Memory, and Disk Usage: Check the resource usage on the nodes running the application. High CPU, memory, or disk I/O usage can indicate bottlenecks. Network Utilization: Check network usage, especially if the job involves significant data transfer between nodes. 3. Examine YARN and Application Logs YARN Logs: Access the logs through the YARN ResourceManager web UI. Look for errors, warnings, and unusual delays. Application Master (AM) Logs: Review the AM logs for any signs of retries, timeouts, or other issues. Container Logs: Check the logs of individual containers for errors and performance issues. 4. Check for Resource Contention NodeManager Logs: Look for signs of resource contention, such as high wait times for container allocation. Cluster Load: Check if other jobs are running concurrently and consuming significant resources. 5. Investigate Job Configuration Parallelism: Ensure the job is correctly configured for parallel execution (e.g., number of mappers and reducers in a MapReduce job). Resource Allocation: Verify that the job has sufficient resources allocated (e.g., memory, vCores). 6. Data Skew and Distribution Data Skew: Analyze the input data for skew. Uneven data distribution can cause some tasks to take much longer than others. Task Distribution: Check if certain tasks or stages are taking disproportionately longer. 7. Network and I/O Bottlenecks Shuffle and Sort Phase: In Hadoop and Spark, the shuffle phase can be a bottleneck. Monitor the shuffle performance and look for skew or excessive data transfer. HDFS or Storage I/O: Ensure that the underlying storage (HDFS, S3, etc.) is performing optimally and there are no bottlenecks. 8. Garbage Collection and JVM Tuning GC Logs: If the application is JVM-based, check the garbage collection logs for excessive GC pauses. JVM Heap Size: Verify that the JVM heap size is appropriately configured to avoid frequent GC. 9. Configuration Parameters and Tuning YARN Configuration: Check for misconfigurations in YARN resource allocation settings. Application-specific Tuning: Tune parameters specific to the application framework (e.g., Spark, MapReduce). 10. External Dependencies External Services: If the application interacts with external services (e.g., databases, APIs), ensure they are not the bottleneck. Dependency Failures: Look for timeouts or failures in external service calls. Detailed Steps for Specific Frameworks For Hadoop MapReduce Jobs Check Job History Server: Analyze the job in the Job History Server web UI. Identify slow tasks and investigate their logs. Analyze Task Attempts: Look for tasks that have failed and retried multiple times. Identify tasks with unusually high execution times. For Apache Spark Jobs Spark UI: Use the Spark web UI to analyze stages, tasks, and jobs. Look for stages that have long task durations or high task counts. Executor Logs: Check the logs of individual Spark executors for errors and warnings. Driver Logs: Examine the driver logs for signs of job bottlenecks or delays. Conclusion Systematically troubleshooting a job that is taking longer than usual involves a combination of monitoring resource utilization, examining logs, analyzing job configurations, and investigating data distribution and skew. By following these steps and using the right tools, you can identify and resolve the performance bottlenecks effectively. If the issue persists, consider breaking down the problem further or seeking help from more detailed profiling tools or experts familiar with your specific application framework and environment. ... View more

  Data Loss: When you perform an INSERT OVERWRITE operation in Hive, it completely replaces the data in the target table or partition. if the data is not correctly inserted, it can result in data loss. Column Qualifiers: HBase stores data in a key-value format with rows, column families, and column qualifiers. Issues with specific column qualifiers could be due to schema mismatches or data type incompatibilities. Upserting Data: Upserting (update or insert) in HBase via Hive can be challenging since Hive primarily supports batch processing and doesn't have native support for upsert operations directly. As HBASE handlers tables are external tables.  Best Practices and Troubleshooting Schema Matching: Ensure that the schema of the Hive table and the HBase table matches, especially the data types and column qualifiers. Data Types: Be cautious with data types. HBase stores everything as bytes, so type conversions must be handled properly. Error Handling: Implement proper error handling and logging to identify issues during data insertion.   ... View more

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The error message indicates Tableau is having trouble connecting to your "ShowData" data source and there's an issue with the SQL query it's trying to run on your Hive database. Let's break down the error and potential solutions: Error Breakdown: Bad Connection: Tableau can't establish a connection to the Hive database. Error Code: B19090E0: Generic Tableau error for connection issues. Error Code: 10002: Hive specific error related to the SQL query. SQL state: TStatus(statusCode:ERROR_STATUS): Hive is encountering an error during query processing. Invalid column reference 'tableausql.fieldname': The specific error points to an invalid column reference in the query. Potential Solutions: Verify Database Connection: Ensure the Hive server is running and accessible from Tableau. Double-check the connection details in your Tableau data source configuration, including server address, port, username, and password. Review SQL Query: The error message highlights "tableausql.fieldname" as an invalid column reference. Check if this field name actually exists in your Hive table. There might be a typo or a case-sensitivity issue. If "tableausql" is a prefix Tableau adds, ensure it's not causing conflicts with your actual column names. Check for Unsupported Functions: In rare cases, Tableau might try to use functions not supported by Hive. ... View more

Hi @Sidhartha  Could you please try the following sample def convertDatatype(datatype: String): DataType = { val convert = datatype match { case "string" => StringType case "short" => ShortType case "int" => IntegerType case "bigint" => LongType case "float" => FloatType case "double" => DoubleType case "decimal" => DecimalType(38,30) case "date" => TimestampType case "boolean" => BooleanType case "timestamp" => TimestampType } convert } val input_data = List(Row(1l, "Ranga", 27, BigDecimal(306.000000000000000000)), Row(2l, "Nishanth", 6, BigDecimal(606.000000000000000000))) val input_rdd = spark.sparkContext.parallelize(input_data) val hiveCols="id:bigint,name:string,age:int,salary:decimal" val schemaList = hiveCols.split(",") val schemaStructType = new StructType(schemaList.map(col => col.split(":")).map(e => StructField(e(0), convertDatatype(e(1)), true))) val myDF = spark.createDataFrame(input_rdd, schemaStructType) myDF.printSchema() myDF.show() val myDF2 = myDF.withColumn("new_salary", col("salary").cast("double")) myDF2.printSchema() myDF2.show() ... View more

It seems like there might be an issue with the way you're using single quotes in the loop. The variable eachline should be expanded, but it won't if it's enclosed in single quotes. Try using double quotes around the variable and see if that resolves the issue. Here's the corrected loop:       for eachline in "${testarray[@]}" do beeline -f "${eachline}.sql" done   This way, the value of eachline will be correctly expanded when constructing the command. Also, ensure that the SQL files are present in the correct path or provide the full path if needed. If the issue persists, please provide more details on the error message or behavior you're experiencing for further assistance. ... View more

Hive typically relies on the schema definition provided during table creation, and it doesn't perform automatic type conversion while loading data. If there's a mismatch between the data type in the CSV file and the expected data type in the Hive table, it may result in null or incorrect values. Use the CAST function to explicitly convert the data types during the INSERT statement.   INSERT INTO TABLE target_table SELECT CAST(column1 AS INT), CAST(column2 AS STRING), ... FROM source_table;   Preprocess your CSV data before loading it into Hive. You can use tools like Apache NiFi or custom scripts to clean and validate the data before ingestion. Remember to thoroughly validate and clean your data before loading it into Hive to avoid unexpected issues. Also, the choice of method depends on your specific use case and the level of control you want over the data loading process. ... View more

Please review the fs.defaultFS configuration in the core-site.xml file within the Hive process directory and ensure that it does not contain any leading or trailing spaces. ... View more

Make sure dfprocessed datafrmae doesn't contains any empty rows.  In Spark, you can identify and filter out empty rows in a DataFrame using the filter operation. Empty rows typically have null or empty values across all columns.   // Identify and filter out empty rows val nonEmptyRowsDF = df.filter(not(df.columns.map(col(_).isNull).reduce(_ || _)))   This code uses the filter operation along with the not function and a condition that checks if any column in a row is null. It then removes rows where all columns are null or empty. If you want to check for emptiness based on specific columns, you can specify those columns in the condition:   val columnsToCheck = Array("column1", "column2", "column3") val nonEmptyRowsDF = df.filter(not(columnsToCheck.map(col(_).isNull).reduce(_ || _)))   Adjust the column names based on your DataFrame structure. The resulting nonEmptyRowsDF will contain rows that do not have null or empty values in the specified columns. ... View more