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09-16-2021
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My Accepted Solutions
| Title | Views | Posted |
|---|---|---|
| 1301 | 10-22-2025 05:48 AM | |
| 1379 | 09-05-2025 07:19 AM | |
| 2337 | 07-15-2025 02:22 AM | |
| 3198 | 05-22-2025 03:00 AM | |
| 2004 | 05-19-2025 03:02 AM |
01-08-2024
09:42 PM
Observing the provided snippet, it's evident that the job with ID application_1666660764861_0196 is currently in progress. To gather more insights into the ongoing Sqoop job, please review the progress details of this specific application (application_1666660764861_0196) . Check YARN ResourceManager Web UI: Open your web browser and navigate to the YARN ResourceManager Web UI , Look for the specific job ID mentioned in the logs (application_1666660764861_0196). This UI provides details about the running job, its progress, and any errors. Review Hadoop Cluster Logs: Examine the Hadoop cluster logs for any potential issues. Hadoop logs can provide insights into resource constraints, node failures, or other problems that might be affecting your job. Check Database Connection: Ensure that the database you are importing from is accessible and that the connection parameters (such as username, password, JDBC URL) are correct. Sometimes, jobs can hang if there are issues with the database. Verify Network Connectivity: Ensure that there are no network issues between the cluster nodes and the database. Check for any firewalls or network restrictions that might be impacting connectivity. Resource Utilization: Check the resource utilization on your Hadoop cluster. Ensure that there are enough resources (CPU, memory) available for the job to run. By systematically checking these areas, you should be able to gather more information about why the Sqoop job is still running and address any issues that may be preventing it from completing successfully.
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12-26-2023
10:18 PM
1 Kudo
You're right to be concerned about password security, especially in a distributed environment. Spark doesn't inherently provide a built-in secure password handling mechanism like the Hadoop Credential Provider, but there are several approaches you can consider to enhance security when dealing with passwords: Credential Providers: While Spark itself doesn't have a native credential provider, you might consider using Hadoop Credential Providers in combination with Spark. You can store sensitive information like passwords in Hadoop's CredentialProvider API. Then, you'd access these securely stored credentials in your Spark job. Environment Variables: You can set the password as an environment variable on the cluster or machine running Spark. Accessing environment variables in your Spark code helps avoid directly specifying passwords in code or configuration files. Key Management Services (KMS): Some cloud providers offer key management services that allow you to securely store and manage credentials. You can retrieve these credentials dynamically in your Spark application. Secure Storage Systems: Leverage secure storage systems or secret management tools like HashiCorp Vault, AWS Secrets Manager, or Azure Key Vault. These tools provide secure storage for sensitive information and offer APIs to retrieve credentials when needed by your application. Secure File Systems: Utilize secure file systems or encryption mechanisms to protect sensitive configuration files. These files could contain the necessary credentials, and access could be restricted using appropriate permissions. Encryption and Secure Communication: Ensure that communication between Spark and external systems is encrypted (e.g., using SSL/TLS) to prevent eavesdropping on the network. Token-Based Authentication: Whenever possible, consider using token-based authentication mechanisms instead of passwords. Tokens can be time-limited and are generally safer for communication over the network. When implementing these measures, it's crucial to balance security with convenience and operational complexity. Choose the approach that aligns best with your security policies, deployment environment, and ease of management for your use case.
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11-21-2023
09:37 AM
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.
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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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11-09-2023
01:41 AM
Hi @HadoopHero , If the query involves dynamic partitioning, one potential issue is that 'hive.optimize.sort.dynamic.partition.threshold' may limit the number of open record writers to just one per partition value, resulting in the creation of only one file. To investigate this, could you attempt disabling 'hive.optimize.sort.dynamic.partition.threshold' entirely? SET hive.optimize.sort.dynamic.partition.threshold=-1; Note : The problem statement contains a typo in the config name
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11-06-2023
05:11 AM
The error message "HiveServer2Error: ImpalaRuntimeException: Error making 'add_partitions' RPC to Hive Metastore" typically indicates a problem when Impala, a distributed SQL query engine, tries to interact with the Hive Metastore service to add partitions. This error can be caused by several factors, and it usually points to an issue with the Hive Metastore service or the interaction between Impala and Hive. Here are some common causes and troubleshooting steps for this error: Hive Metastore Service Issues: Check if the Hive Metastore service is up and running. You should ensure that the Hive Metastore service is started and healthy. Verify that the Hive Metastore service is reachable from the machine where Impala is running. Network issues or firewall rules could prevent proper communication. Metastore Configuration: Verify the Metastore configuration in the Impala configuration files (impala-site.xml). Ensure that the Metastore URIs and authentication settings are correctly configured. Metastore Database Issues: Check the health and availability of the underlying database used by the Hive Metastore. Ensure that it's accessible, and there are no database connection issues. Verify that the Metastore database is not overwhelmed or experiencing performance problems. Authorization and Authentication: Verify that the Impala service has the necessary privileges and permissions to interact with the Hive Metastore. Check if Kerberos authentication is enabled, and ensure that the necessary credentials and keytabs are correctly configured. Log Analysis: Examine the logs of both Impala and Hive Metastore services for more detailed error messages. The logs may provide additional information about the root cause of the issue. Resource Limitations: Check if there are any resource limitations (e.g., memory, CPU) on the machines running Impala and the Hive Metastore. Resource shortages can lead to RPC failures. Software Versions: Ensure that Impala and Hive are compatible in terms of versions and dependencies. An incompatible combination of software versions can lead to errors. Cluster Issues: If you are running Impala in a distributed cluster, verify the overall health of the cluster. Other cluster-level issues can sometimes affect the interaction with the Hive Metastore. Network Issues: Check for network-related problems, such as DNS resolution or proxy settings, which can impede communication between Impala and the Hive Metastore. Database Locks: Database locks in the Metastore can sometimes cause issues. Check if there are any locks in the Hive Metastore database. If you have access to detailed logs or additional error messages, those can be particularly helpful in diagnosing the specific problem that led to this error. Depending on your environment and configurations, the resolution may involve addressing one or more of the above factors.
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11-06-2023
03:10 AM
1 Kudo
The behavior you're observing is related to the precision differences between STRING and FLOAT data types. When you cast a STRING to a FLOAT, Hive attempts to interpret and represent the value as accurately as possible within the constraints of a FLOAT data type. FLOATs are limited in precision, and the fractional part might not be represented exactly. In your example, "5724.95" in FLOAT was stored as "5724.9501953125." This discrepancy is due to the way binary floating-point numbers work and how they might not be able to precisely represent certain decimal values. If you need exact decimal representation, you should consider using a DECIMAL data type instead of FLOAT. DECIMAL provides higher precision and is better suited for scenarios where you need to maintain the exact decimal value without potential loss of precision. Here's how you can cast your STRING column to DECIMAL to preserve the exact decimal value: SELECT a, CAST(a AS DECIMAL(20, 10)) AS exact_value FROM your_table; In this example, DECIMAL(20, 10) indicates a decimal type with a total width of 20 digits and 10 decimal places. This will preserve the exact decimal representation you need. Keep in mind that DECIMAL has higher storage requirements compared to FLOAT because it maintains precision, so choose the appropriate data type based on your requirements. Example : 0: jdbc:hive2://nightly-71x-zg-2.nightly-71x-> SELECT CAST('5724.9501953125' AS DECIMAL(20, 10)) AS decimal_value ;
INFO : Compiling command(queryId=hive_20231106110627_aaa98b66-4db6-4307-9be3-598018c13fbf): SELECT CAST('5724.9501953125' AS DECIMAL(20, 10)) AS decimal_value
INFO : Semantic Analysis Completed (retrial = false)
INFO : Created Hive schema: Schema(fieldSchemas:[FieldSchema(name:decimal_value, type:decimal(20,10), comment:null)], properties:null)
INFO : Completed compiling command(queryId=hive_20231106110627_aaa98b66-4db6-4307-9be3-598018c13fbf); Time taken: 0.062 seconds
INFO : Executing command(queryId=hive_20231106110627_aaa98b66-4db6-4307-9be3-598018c13fbf): SELECT CAST('5724.9501953125' AS DECIMAL(20, 10)) AS decimal_value
INFO : Completed executing command(queryId=hive_20231106110627_aaa98b66-4db6-4307-9be3-598018c13fbf); Time taken: 0.006 seconds
INFO : OK
+------------------+
| decimal_value |
+------------------+
| 5724.9501953125 |
+------------------+
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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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