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09-16-2021
344
Posts
53
Kudos Received
27
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My Accepted Solutions
| Title | Views | Posted |
|---|---|---|
| 342 | 11-22-2024 05:29 AM | |
| 180 | 11-15-2024 06:38 AM | |
| 403 | 11-13-2024 07:12 AM | |
| 454 | 11-10-2024 11:19 PM | |
| 629 | 10-25-2024 05:02 AM |
11-21-2023
06:30 AM
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.
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11-21-2023
06:10 AM
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."
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11-21-2023
05:43 AM
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.
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11-21-2023
05:38 AM
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
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11-16-2023
03:11 AM
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
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11-08-2023
01:17 AM
1 Kudo
To add to the point of @ggangadharan, there are lots of good articles/posts why the float and even the double datatype has these problems. Note that this is not Hive / Hadoop or Java specific. https://stackoverflow.com/questions/3730019/why-not-use-double-or-float-to-represent-currency https://dzone.com/articles/never-use-float-and-double-for-monetary-calculatio https://www.red-gate.com/hub/product-learning/sql-prompt/the-dangers-of-using-float-or-real-datatypes Miklos
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10-31-2023
12:21 AM
The error message you're encountering, "java.util.LinkedList cannot be cast to org.apache.hive.hcatalog.mapreduce.InputJobInfo," typically occurs when there's a mismatch between the data types or structures during a Sqoop import from MySQL to Hive. Here are some steps to troubleshoot and potentially resolve the issue: Check Hive and HCatalog Compatibility: Ensure that the versions of Hive and HCatalog you are using are compatible with your version of Sqoop. If there's a version mismatch, consider updating or downgrading one of them to ensure compatibility. Check Your SQL Query: Review the SQL query you're using with Sqoop to import data. Ensure that it's correctly configured and that the source and target tables are correctly specified. Check Data Types: Ensure that the data types of the source MySQL table match the data types of the target Hive table. Inconsistencies in data types can lead to this error. Check Field Mapping: Verify that the fields in your MySQL table match the columns in your Hive table in terms of number and order. Ensure there are no extra or missing columns.
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10-30-2023
07:17 AM
It appears that the JSON data contains multiple application entries within a single line, presented as struct data. This format makes schema creation challenging. To address this, you can leverage Spark to flatten the schema and store the data in Hive. This enables you to query the data conveniently from either Hive or Spark. Read the data JSON data df = spark.read.json("/user/hive/app_data_sample_data.json") First, explode the "app" array to separate rows from pyspark.sql.functions import col, explode, lit, struct
exploded_df = df.select(
explode(col("apps.app")).alias("app")
) Flatten and transform the exploded DataFrame # Flatten and transform the exploded DataFrame
flattened_df = exploded_df.select(
col("app.id").alias("id"),
col("app.user").alias("user"),
col("app.name").alias("name"),
col("app.queue").alias("queue"),
col("app.state").alias("state"),
col("app.finalstatus").alias("finalstatus"),
col("app.progress").alias("progress"),
col("app.trackingui").alias("trackingui"),
col("app.trackingurl").alias("trackingurl"),
col("app.diagnostics").alias("diagnostics"),
col("app.clusterid").alias("clusterid"),
col("app.applicationtype").alias("applicationtype"),
col("app.applicationtags").alias("applicationtags"),
col("app.priority").alias("priority"),
col("app.startedtime").alias("startedtime"),
col("app.launchtime").alias("launchtime"),
col("app.finishedtime").alias("finishedtime"),
col("app.elapsedtime").alias("elapsedtime"),
col("app.amcontainerlogs").alias("amcontainerlogs"),
col("app.amhosthttpaddress").alias("amhosthttpaddress"),
col("app.amrpcaddress").alias("amrpcaddress"),
col("app.masternodeid").alias("masternodeid"),
col("app.allocatedmb").alias("allocatedmb"),
col("app.allocatedvcores").alias("allocatedvcores"),
col("app.reservedmb").alias("reservedmb"),
col("app.reservedvcores").alias("reservedvcores"),
col("app.runningcontainers").alias("runningcontainers"),
col("app.memoryseconds").alias("memoryseconds"),
col("app.vcoreseconds").alias("vcoreseconds"),
col("app.queueusagepercentage").alias("queueusagepercentage"),
col("app.clusterusagepercentage").alias("clusterusagepercentage"),
col("app.preemptedresourcemb").alias("preemptedresourcemb"),
col("app.preemptedresourcevcores").alias("preemptedresourcevcores"),
col("app.numnonamcontainerpreempted").alias("numnonamcontainerpreempted"),
col("app.numamcontainerpreempted").alias("numamcontainerpreempted"),
col("app.preemptedmemoryseconds").alias("preemptedmemoryseconds"),
col("app.preemptedvcoreseconds").alias("preemptedvcoreseconds"),
col("app.logaggregationstatus").alias("logaggregationstatus"),
col("app.unmanagedapplication").alias("unmanagedapplication"),
col("app.amnodelabelexpression").alias("amnodelabelexpression"),
struct(
lit("lifetime").alias("type"),
lit("unlimited").alias("expirytime"),
lit(-1).alias("remainingtimeinseconds")
).alias("timeouts")
) Validate the flattened DataFrame flattened_df.show(truncate=False) If the data looks good , save the data as table. flattened_df.write.mode('overwrite').saveAsTable("app_data") Query form hive (beeline) +---------------------------------+----------------+-------------------------------------------+-------------------+-----------------+-----------------------+--------------------+----------------------+----------------------------------------------------+----------------------------------------------------+---------------------+---------------------------+----------------------------------------------------+--------------------+-----------------------+----------------------+------------------------+-----------------------+----------------------------------------------------+-----------------------------+------------------------+------------------------+-----------------------+---------------------------+----------------------+--------------------------+-----------------------------+-------------------------+------------------------+--------------------------------+----------------------------------+-------------------------------+-----------------------------------+--------------------------------------+-----------------------------------+----------------------------------+---------------------------------+--------------------------------+--------------------------------+---------------------------------+----------------------------------------------------+
| app_data.id | app_data.user | app_data.name | app_data.queue | app_data.state | app_data.finalstatus | app_data.progress | app_data.trackingui | app_data.trackingurl | app_data.diagnostics | app_data.clusterid | app_data.applicationtype | app_data.applicationtags | app_data.priority | app_data.startedtime | app_data.launchtime | app_data.finishedtime | app_data.elapsedtime | app_data.amcontainerlogs | app_data.amhosthttpaddress | app_data.amrpcaddress | app_data.masternodeid | app_data.allocatedmb | app_data.allocatedvcores | app_data.reservedmb | app_data.reservedvcores | app_data.runningcontainers | app_data.memoryseconds | app_data.vcoreseconds | app_data.queueusagepercentage | app_data.clusterusagepercentage | app_data.preemptedresourcemb | app_data.preemptedresourcevcores | app_data.numnonamcontainerpreempted | app_data.numamcontainerpreempted | app_data.preemptedmemoryseconds | app_data.preemptedvcoreseconds | app_data.logaggregationstatus | app_data.unmanagedapplication | app_data.amnodelabelexpression | app_data.timeouts |
+---------------------------------+----------------+-------------------------------------------+-------------------+-----------------+-----------------------+--------------------+----------------------+----------------------------------------------------+----------------------------------------------------+---------------------+---------------------------+----------------------------------------------------+--------------------+-----------------------+----------------------+------------------------+-----------------------+----------------------------------------------------+-----------------------------+------------------------+------------------------+-----------------------+---------------------------+----------------------+--------------------------+-----------------------------+-------------------------+------------------------+--------------------------------+----------------------------------+-------------------------------+-----------------------------------+--------------------------------------+-----------------------------------+----------------------------------+---------------------------------+--------------------------------+--------------------------------+---------------------------------+----------------------------------------------------+
| application_282828282828_12717 | xyz | xyz-4b6bdae2-1a0c-4772-bd8e-0d7454268b82 | root.users.dummy | finished | succeeded | 100.0 | history | http://dang:8088/proxy/application_282828282828_12717/ | session stats:submitteddags=1, successfuldags=1, faileddags=0, killeddags=0
| 282828282828 | aquaman | ABC,xyz_20221107070124_2beb5d90-24c7-4b1b-b977-3c9af1397195,userid=dummy | 0 | 1667822485626 | 1667822485767 | 1667822553365 | 67739 | http://dingdong:8042/node/containerlogs/container_e65_282828282828_12717_01_000001/xyz | dingdong:8042 | dingdong:46457 | dingdong:8041 | -1 | -1 | -1 | -1 | -1 | 1264304 | 79 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | succeeded | false | | {"type":"lifetime","expirytime":"unlimited","remainingtimeinseconds":-1} |
| application_282828282828_12724 | xyz | xyz-94962a3e-d230-4fd0-b68b-01b59dd3299d | root.users.dummy | finished | succeeded | 100.0 | history | http://dang:8088/proxy/application_282828282828_12724/ | session stats:submitteddags=1, successfuldags=1, faileddags=0, killeddags=0
| 282828282828 | aquaman | ZZZ_,xyz_20221107070301_e6f788db-e39c-49b6-97d5-6a02ff994c00,userid=dummy | 0 | 1667822585231 | 1667822585437 | 1667822631435 | 46204 | http://ding:8042/node/containerlogs/container_e65_282828282828_12724_01_000002/xyz | ding:8042 | ding:46648 | ding:8041 | -1 | -1 | -1 | -1 | -1 | 5603339 | 430 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | time_out | false | | {"type":"lifetime","expirytime":"unlimited","remainingtimeinseconds":-1} |
| application_282828282828_12736 | xyz | xyz-1a9c73ef-2992-40a5-aaad-9f0688bb04f4 | root.users.dummy | finished | succeeded | 100.0 | history | http://dang:8088/proxy/application_282828282828_12736/ | session stats:submitteddags=1, successfuldags=1, faileddags=0, killeddags=0
| 282828282828 | aquaman | BLAHBLAH,xyz_20221107070609_8d261352-3efa-46c5-a5a0-8a3cd745d180,userid=dummy | 0 | 1667822771170 | 1667822773663 | 1667822820351 | 49181 | http://dong:8042/node/containerlogs/container_e65_282828282828_12736_01_000001/xyz | dong:8042 | dong:34266 | dong:8041 | -1 | -1 | -1 | -1 | -1 | 1300011 | 89 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | succeeded | false | | {"type":"lifetime","expirytime":"unlimited","remainingtimeinseconds":-1} |
| application_282828282828_12735 | xyz | xyz-d5f56a0a-9c6b-4651-8f88-6eaff5953777 | root.users.dummy | finished | succeeded | 100.0 | history | http://dang:8088/proxy/application_282828282828_12735/ | session stats:submitteddags=1, successfuldags=1, faileddags=0, killeddags=0
| 282828282828 | aquaman | HAHAHA_,xyz_20221107070605_a082d9d8-912f-4278-a2ef-5dfe66089fd7,userid=dummy | 0 | 1667822766897 | 1667822766999 | 1667822796759 | 29862 | http://dung:8042/node/containerlogs/container_e65_282828282828_12735_01_000001/xyz | dung:8042 | dung:42765 | dung:8041 | -1 | -1 | -1 | -1 | -1 | 669695 | 44 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | succeeded | false | | {"type":"lifetime","expirytime":"unlimited","remainingtimeinseconds":-1} |
+---------------------------------+----------------+-------------------------------------------+-------------------+-----------------+-----------------------+--------------------+----------------------+----------------------------------------------------+----------------------------------------------------+---------------------+---------------------------+----------------------------------------------------+--------------------+-----------------------+----------------------+------------------------+-----------------------+----------------------------------------------------+-----------------------------+------------------------+------------------------+-----------------------+---------------------------+----------------------+--------------------------+-----------------------------+-------------------------+------------------------+--------------------------------+----------------------------------+-------------------------------+-----------------------------------+--------------------------------------+-----------------------------------+----------------------------------+---------------------------------+--------------------------------+--------------------------------+---------------------------------+----------------------------------------------------+
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10-27-2023
12:07 AM
The error message "Unknown HS2 problem when communicating with Thrift server" typically indicates that there is an issue when trying to communicate with the Hive Server 2 (HS2) through its Thrift interface. This error can occur for various reasons, and troubleshooting it may require some investigation. Here are some common steps to help resolve this issue: Check Hive Server Status: Ensure that the Hive Server 2 is up and running and that it's reachable from your client. You can check its status and logs to see if there are any errors or issues reported. Network Connectivity: Verify that there are no network-related issues that might be preventing your client from connecting to the Hive Server. Check firewalls, network configurations, and any potential network interruptions. Hive Configuration: Review the Hive server's configuration to ensure it's correctly set up. Pay attention to security configurations, like authentication and authorization settings. Thrift Protocol Version: Ensure that the Thrift protocol version used by your client matches the version supported by the Hive Server. Mismatched protocol versions can lead to communication problems. Client-Side Issues: Check the client application or code you are using to interact with Hive. Ensure that it's properly configured and making the correct requests to the Hive Server. Logs and Error Messages: Examine the logs and error messages in more detail to get specific information about what might be causing the problem. This can help pinpoint the issue. Server Version Compatibility: Ensure that the client and server components (Hive client and Hive Server) are compatible in terms of versions. Incompatible versions can lead to communication issues. Authentication and Authorization: If your Hive server is configured with authentication and authorization, ensure that you have the necessary permissions and credentials to access it. Load and Resource Constraints: Check if the Hive Server is under heavy load or if there are resource constraints that might be affecting its ability to respond to client requests. Driver and Libraries: Ensure that you are using the correct driver or libraries for your client application. If you're using JDBC or ODBC, make sure the corresponding driver is installed and configured correctly. If you continue to face issues after performing these checks, it may be necessary to provide more specific error messages or details about your environment to diagnose the problem further.
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10-26-2023
11:49 PM
Please follow the below article and validate the same. https://my.cloudera.com/knowledge/How-to-configure-HDFS-and-Hive-to-use-different-JCEKS-and?id=326056
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