The stacktrace indicates a resemblance to the issue reported in https://issues.apache.org/jira/browse/HIVE-21698. To address this, it is recommended to upgrade to CDP version 7.1.7 or a higher release ... View more

Ingesting data from MongoDB into a Cloudera data warehouse, particularly Cloudera's CDH (Cloudera Distribution including Apache Hadoop), involves making decisions about data modeling and choosing the right approach based on your use case and requirements. Considerations: Schema Design: MongoDB is a NoSQL database with a flexible schema, allowing documents in a collection to have different structures. If your goal is to maintain the flexibility and take advantage of the dynamic nature of MongoDB, you might consider storing documents as-is. Data Modeling: Decide whether you want to maintain a document-oriented model or convert the data to a more relational model. The decision may depend on your analysis and reporting requirements. Storage Format: In Cloudera environments, data is often stored in formats like Parquet or Avro. Consider the storage format that aligns with your performance and storage requirements. HBaseStorageHandler: Apache HBase along with HBaseStorageHandler for ingesting data from MongoDB into Cloudera. This approach involves storing the data in HBase tables and utilizing the HBaseStorageHandler to integrate HBase with Apache Hive. Approaches: Direct Import of MongoDB Documents: In this approach, you ingest data directly from MongoDB into Cloudera. Tools like Apache Sqoop or MongoDB Connector for Hadoop can be used for this purpose. The documents will be stored as-is in the Hive tables, allowing you to query unstructured data. Converting MongoDB Documents to Relational Model: Another approach involves converting MongoDB documents to a more structured, tabular format before ingesting into Cloudera. This conversion could be done using an ETL (Extract, Transform, Load) tool or a custom script. This approach may be suitable if you have a specific schema in mind or if you want to leverage traditional SQL querying. Querying Unstructured Data: If you choose to import MongoDB documents as-is, you can still query unstructured data using tools like Apache Hive or Apache Impala. Both support querying data stored in various formats, including JSON. You can perform nested queries and navigate through the document structure. Steps: Direct Import: Use a tool like Apache Sqoop or MongoDB Connector for Hadoop to import data directly into Cloudera. Define Hive external tables to map to the MongoDB collections. Convert and Import: If you choose to convert, use an ETL tool like Apache NiFi or custom scripts to transform MongoDB documents into a structured format. Import the transformed data into Cloudera. Querying: Use Hive or Impala to query the imported data. For complex nested structures, explore Hive's support for JSON functions Direct Import into HBase: Use tools like Apache NiFi or custom scripts to extract data from MongoDB. Transform the data into a suitable format for HBase, keeping in mind HBase's column-oriented storage. Import the transformed data directly into HBase tables. Integration with Hive using HBaseStorageHandler: Create an external Hive table using the HBaseStorageHandler. Define the mapping between the Hive table and the HBase table. Example: Here's a simplified example of how you might create an external Hive table with HBaseStorageHandler: -- Create an external Hive table with HBaseStorageHandler CREATE EXTERNAL TABLE hbase_mongo_data ( id INT, name STRING, details STRUCT<field1:STRING, field2:INT, ...>, -- Define the nested structure ... ) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH SERDEPROPERTIES ( "hbase.columns.mapping" = ":key,cf:col1,cf:col2,details:field1,details:field2,..." ) TBLPROPERTIES ( "hbase.table.name" = "your_hbase_table_name" ); Benefits and Considerations: HBase's Schema Flexibility: HBase provides schema flexibility, which can accommodate the dynamic structure of MongoDB documents. You can define column families and qualifiers dynamically. HBaseStorageHandler: The HBaseStorageHandler allows you to interact with HBase tables using Hive, making it easier to query data using SQL-like syntax. Integration with Cloudera Ecosystem: HBase is part of the Cloudera ecosystem, and integrating it with Hive allows you to leverage the strengths of both technologies. Querying Data: Hive queries can directly access data in HBase tables using HBaseStorageHandler. You can use Hive's SQL-like syntax for querying, and it provides some support for nested structures. Connect Tableau to Hive: Use Tableau to connect to the external Hive table with HBaseStorageHandler. Tableau supports Hive as a data source, and you can visualize the data using Tableau's capabilities. Optimize for Performance: Depending on the size of your data, consider optimizing the HBase schema, indexing, and caching to enhance query performance. Consideration for Tableau: Tableau supports direct connectivity to Hive or Impala, allowing you to visualize and analyze the data stored in Cloudera. Ensure that the data format and structure are suitable for Tableau consumption. Conclusion: The best approach depends on your specific use case, requirements, and the level of flexibility you need in handling the MongoDB documents. If the dynamic nature of MongoDB documents is essential for your analysis, direct import with subsequent querying might be a suitable choice. If a more structured approach is needed, consider conversion before ingestion,Using HBase along with HBaseStorageHandler in Hive provides a powerful and flexible solution for integrating MongoDB data into the Cloudera ecosystem. This approach leverages the strengths of both HBase and Hive while enabling seamless integration with tools like Tableau for visualization. ... View more

The error you're encountering (OperationalError: TExecuteStatementResp(status=TStatus(statusCode=3, ...) indicates that there was an issue during the execution of the Hive query. The specific error message within the response is Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.tez.TezTask. Here are a few steps you can take to troubleshoot and resolve the issue: Check Hive Query Logs: Review the Hive query logs to get more details about the error. The logs might provide information about the specific query or task that failed, including any error messages or stack traces. You can find the logs in the Hive logs directory. The location may vary based on your Hadoop distribution and configuration. Inspect Query Syntax: Double-check the syntax of your Hive SQL query. Ensure that the query is valid and properly formed. Sometimes, a syntax error can lead to execution failures. Verify Hive Table Existence: Confirm that the Hive table you're querying actually exists. If the table or the specified database is missing, it can lead to errors. Check Permissions: Verify that the user running the Python query has the necessary permissions to access and query the Hive table. Lack of permissions can result in execution errors. Examine Tez Configuration: If your Hive queries use the Tez execution engine, check the Tez configuration. Ensure that Tez is properly configured on your cluster and that there are no issues with the Tez execution. Look for Resource Constraints: The error message mentions TezTask, so consider checking if there are any resource constraints on the Tez execution, such as memory or container size limitations. Update Python Library: Ensure that you are using a compatible version of the Python library for interacting with Hive (e.g., pyhive or pyhive[hive]). Updating the library to the latest version might help resolve certain issues. Test with a Simple Query: Simplify your query to a basic one and see if it executes successfully. This can help isolate whether the issue is specific to the query or a more general problem. After reviewing the logs and checking the mentioned aspects, you should have more insights into what might be causing the error. If the issue persists, consider providing more details about the Hive query and the surrounding context, so we can offer more targeted assistance. ... View more

It seems like you want to run the Tez example "OrderedWordCount" using the tez-examples*.jar file. The OrderedWordCount example is part of the Tez examples and demonstrates how to perform a word count with ordering. Assuming you have Tez installed on your system, you can follow these steps:   export TEZ_CONF_DIR=/etc/tez/conf/ export TEZ_HOME=/opt/cloudera/parcels/CDH/lib/tez/ export HADOOP_CLASSPATH=${TEZ_CONF_DIR}:${TEZ_HOME}/bin/*:${TEZ_HOME}/* yarn jar ${TEZ_HOME}/bin/tez-examples-*.jar orderedwordcount /somewhere/input /somewhere/output   ... View more

The error message indicates that there is an issue related to resource allocation in YARN, the resource manager in Hadoop. Specifically, the error mentions that the requested resource exceeds the maximum allowed allocation. Here are some steps you can take to address this issue: Review YARN Configuration: Check the YARN configuration settings, particularly those related to resource allocation. Look for properties such as yarn.scheduler.maximum-allocation-mb and yarn.scheduler.maximum-allocation-vcores. Ensure that the values configured for these properties are sufficient for the resources needed by HiveServer2. Increase Maximum Allocation: If the error persists, you might need to increase the maximum allocation for memory and vCores in the YARN scheduler configuration. Update the yarn.scheduler.maximum-allocation-mb and yarn.scheduler.maximum-allocation-vcores properties in the YARN configuration files. Check NodeManager Resources: Verify the resources available on the NodeManagers in your cluster. The maximum allowed allocation is calculated based on the maximum resources of registered NodeManagers. If the NodeManagers have sufficient resources, you can adjust the YARN configuration accordingly. Monitor Resource Usage: Monitor the resource usage in your YARN cluster using tools like the ResourceManager UI or the YARN command-line tools (yarn top, yarn node -list -all, etc.). Identify any patterns of resource exhaustion or contention that could be causing the issue. Review Hive Configuration: Review the Hive configurations related to resource allocation, such as hive.tez.container.size and other relevant settings. Ensure that they are appropriately configured for your cluster. After making any configuration changes, restart the affected services (YARN, HiveServer2) for the changes to take effect.  ... View more

To achieve your goal of loading data from all the latest files in each folder into a single DataFrame, you can collect the file paths from each folder in a list and then load the data into the DataFrame outside the loop. Here's a modified version of your code:   import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ import org.apache.spark.sql.Row val static_path = "/user/hdfs/test/partition_date=" val hours = 3 // Creating list of each folder. val paths = (0 until hours) .map(h => currentTs.minusHours(h)) .map(ts => s"${static_path}${ts.toLocalDate}/hour=${ts.getHour}") .toList // Collect the latest file paths from each folder in a list val latestFilePaths = paths.flatMap { eachfolder => val fs = org.apache.hadoop.fs.FileSystem.get(spark.sparkContext.hadoopConfiguration) val pathstatus = fs.listStatus(new Path(eachfolder)) val currpathfiles = pathstatus.map(x => (x.getPath.toString, x.getModificationTime)) val latestFilePath = currpathfiles .filter(_._1.endsWith(".csv")) .sortBy(_._2) .reverse .headOption .map(_._1) latestFilePath } // Load data from all the latest files into a single DataFrame val df = spark.read.format("csv").load(latestFilePaths: _*) // Show the combined DataFrame df.show()   In this modified code: latestFilePaths is a list that collects the latest file path from each folder. Outside the loop, spark.read.format("csv").load(latestFilePaths: _*) is used to load data from all the latest files into a single DataFrame. Now, df will contain data from all the latest files in each folder, and you can perform further operations or analysis on this combined DataFrame. ... View more

  In Hive, metadata related to tables and columns is typically stored in the 'hive' database, specifically within the 'TBLS' and 'COLUMNS_V2' tables in the 'metastore' database. It is not recommended for users to query the metadata directly. Instead, users can leverage the 'sys' database tables. Here is a modified query that utilizes the 'hive' database tables:   sql   USE sys; -- Get the count of columns for all tables SELECT t.tbl_name AS TABLE_NAME, COUNT(c.column_name) AS COLUMN_COUNT FROM tbls t JOIN columns_v2 c ON t.tbl_id = c.cd_id GROUP BY t.tbl_name;   Explanation: The 'sys.tbls' table contains information about tables, while the 'sys.columns_v2' table contains information about columns. We join these tables on the 'TBL_ID' and 'CD_ID' columns to retrieve information about columns for each table. The 'COUNT(c.COLUMN_NAME)' expression calculates the count of columns for each table. This query provides a list of tables along with the count of columns for each table, using the 'sys' database tables." ... View more

  The error message indicates that there is an inconsistency between the expected schema for the column 'db.table.parameter_11' and the actual schema found in the Parquet file 'hdfs:/path/table/1_data.0.parq'. The column type is expected to be a STRING, but the Parquet schema suggests that it is an optional int64 (integer) column. To resolve this issue, you'll need to investigate and potentially correct the schema mismatch. Here are some steps you can take: Verify the Expected Schema: Check the definition of the 'db.table.parameter_11' column in the Impala metadata or Hive metastore. Ensure that it is defined as a STRING type. Inspect the Parquet File Schema: You can use tools like parquet-tools to inspect the schema of the Parquet file directly. Run the following command in the terminal: bash   parquet-tools schema 1_data.0.parq Look for the 'db.table.parameter_11' column and check its data type in the Parquet schema. Compare Expected vs. Actual Schema: Compare the expected schema for 'db.table.parameter_11' with the actual schema found in the Parquet file. Identify any differences in data types. Investigate Data Inconsistencies: If there are data inconsistencies, investigate how they might have occurred. It's possible that there was a schema evolution or a mismatch during the data writing process. Resolve Schema Mismatch: Depending on your findings, you may need to correct the schema mismatch. This could involve updating the metadata in Impala or Hive to match the actual schema or adjusting the Parquet file schema. Update Impala Statistics: After resolving the schema mismatch, it's a good practice to update Impala statistics for the affected table. This can be done using the COMPUTE STATS command in Impala:   This step ensures that Impala has up-to-date statistics for query optimization. Here's a high-level example of what the Parquet schema inspection might look like: parquet-tools schema 1_data.0.parq Look for the 'db.table.parameter_11' column and check its data type in the Parquet schema. If the data type in the Parquet schema is incorrect, you may need to investigate how the data was written and whether there were any issues during that process. Correcting the schema mismatch and updating Impala statistics should help resolve the issue. ... View more

The error indicates that the Hive Server Interactive (HSI) component is failing to start because the LLAP (Live Long and Process) app associated with it couldn't be started. To troubleshoot and resolve this issue, you can follow these general steps: Check LLAP Log Files: Look into the LLAP log files for more detailed error messages. These log files are typically located in a directory like /var/log/hive or a custom location configured in your environment. Examine the LLAP logs to identify any specific errors or issues that are preventing LLAP from starting. Verify LLAP Configuration: Check the LLAP configuration settings, including memory configurations, queue configurations, and other LLAP-specific parameters. Ensure that the configuration is correct and appropriate for your cluster resources. Verify that there are no typos or errors in the LLAP configuration files . Check Resource Availability: Ensure that there are sufficient resources (memory, CPU, etc.) available on the nodes where LLAP is supposed to run. Verify that LLAP is not competing for resources with other applications or services on the cluster. Check Hive Server Interactive Configuration: Review the configuration settings for Hive Server Interactive. Verify that the LLAP configuration is correctly specified in the Hive Server Interactive configurations. Ensure that the LLAP application name, number of instances, and other LLAP-related settings are accurate. Examine System Logs: Check the system logs on the nodes where LLAP is supposed to run. Look for any system-level issues or errors that might be affecting LLAP startup. Restart LLAP Manually: If LLAP fails to start during Hive Server Interactive startup, consider manually starting LLAP to see if you can get more detailed error messages. You can use commands like hive --service llap --start or the Ambari UI to start LLAP separately. Check for LLAP Process: After trying to start LLAP manually, check if the LLAP process is running. You can use tools like ps or jps to see if the LLAP daemon process is running on the expected nodes. Review Ambari Alerts: Check the Ambari Alerts for any warnings or errors related to Hive Server Interactive or LLAP. Ambari often provides helpful alerts and diagnostics. If the LLAP process is still not starting, the detailed logs and error messages should provide more insights into the root cause of the issue. Addressing the specific error or misconfiguration mentioned in the logs will be crucial in resolving the problem   ... View more

When a Pig job gets stuck after creating the JobID, there could be several reasons for this behavior. Here are some common issues and solutions.  Data Size and Complexity: Check the size and complexity of your data. If the dataset is very large, the storage operation may take a significant amount of time. Optimize your Pig script if possible, and consider processing a smaller subset of the data for testing. Resource Allocation: Ensure that your Hadoop cluster has sufficient resources allocated for the Pig job. Insufficient memory or available resources can lead to job failures or delays. Check the resource configuration in your Hadoop cluster and adjust it accordingly. Job Monitoring: Use Hadoop JobTracker or ResourceManager web interfaces to monitor the progress of your Pig job. This can provide insights into where the job might be stuck. Look for any error messages or warnings in the logs. Logs and Debugging: Examine the Pig logs for any error messages or stack traces. This can help identify the specific issue causing the job to hang. Enable debugging in Pig by adding -Dmapred.job.tracker=<your_job_tracker> to your Pig command, and check the debug logs for more information. Permissions and Path: Ensure that the specified output path /users/emp/empsalinc is writable by the user running the Pig job. Check for any permission issues or typos in the path. Network Issues: Network issues or connectivity problems between nodes in your Hadoop cluster can also cause jobs to hang. Check the network configuration and try running simpler jobs to isolate the issue. Pig Version Compatibility: Ensure that the version of Pig you are using is compatible with your Hadoop distribution. Incompatibility can lead to unexpected issues. Configuration Settings: Review your Pig script and ensure that the configuration settings are appropriate for your environment. Adjust parameters like mapred.job.queue.name, mapreduce.job.queuename, etc., as needed. Custom UDFs: If your Pig script uses custom User Defined Functions (UDFs), ensure that they are correctly implemented and compatible with the version of Pig you are using. By investigating these aspects, you should be able to identify the root cause of the job getting stuck after creating the JobID and take appropriate action to resolve the issue ... View more