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FAQs

TroubleShooting

What are Bad Records?

Records that fail to get loaded into the CarbonData due to data type incompatibility or are empty or have incompatible format are classified as Bad Records.

Where are Bad Records Stored in CarbonData?

The bad records are stored at the location set in carbon.badRecords.location in carbon.properties file. By default carbon.badRecords.location specifies the following location /opt/Carbon/Spark/badrecords.

How to enable Bad Record Logging?

While loading data we can specify the approach to handle Bad Records. In order to analyse the cause of the Bad Records the parameter BAD_RECORDS_LOGGER_ENABLE must be set to value TRUE. There are multiple approaches to handle Bad Records which can be specified by the parameter BAD_RECORDS_ACTION.

  • To pass the incorrect values of the csv rows with NULL value and load the data in CarbonData, set the following in the query :
'BAD_RECORDS_ACTION'='FORCE'
  • To write the Bad Records without passing incorrect values with NULL in the raw csv (set in the parameter carbon.badRecords.location), set the following in the query :
'BAD_RECORDS_ACTION'='REDIRECT'

How to ignore the Bad Records?

To ignore the Bad Records from getting stored in the raw csv, we need to set the following in the query :

'BAD_RECORDS_ACTION'='IGNORE'

How to specify store location while creating carbon session?

The store location specified while creating carbon session is used by the CarbonData to store the meta data like the schema, dictionary files, dictionary meta data and sort indexes.

Try creating carbonsession with storepath specified in the following manner :

val carbon = SparkSession.builder().config(sc.getConf).getOrCreateCarbonSession(<carbon_store_path>)

Example:

val carbon = SparkSession.builder().config(sc.getConf).getOrCreateCarbonSession("hdfs://localhost:9000/carbon/store")

What is Carbon Lock Type?

The Apache CarbonData acquires lock on the files to prevent concurrent operation from modifying the same files. The lock can be of the following types depending on the storage location, for HDFS we specify it to be of type HDFSLOCK. By default it is set to type LOCALLOCK. The property carbon.lock.type configuration specifies the type of lock to be acquired during concurrent operations on table. This property can be set with the following values :

  • LOCALLOCK : This Lock is created on local file system as file. This lock is useful when only one spark driver (thrift server) runs on a machine and no other CarbonData spark application is launched concurrently.
  • HDFSLOCK : This Lock is created on HDFS file system as file. This lock is useful when multiple CarbonData spark applications are launched and no ZooKeeper is running on cluster and the HDFS supports, file based locking.

How to resolve Abstract Method Error?

In order to build CarbonData project it is necessary to specify the spark profile. The spark profile sets the Spark Version. You need to specify the spark version while using Maven to build project.

How Carbon will behave when execute insert operation in abnormal scenarios?

Carbon support insert operation, you can refer to the syntax mentioned in DML Operations on CarbonData. First, create a source table in spark-sql and load data into this created table.

CREATE TABLE source_table(
id String,
name String,
city String)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ",";

SELECT * FROM source_table;
id  name    city
1   jack    beijing
2   erlu    hangzhou
3   davi    shenzhen

Scenario 1 :

Suppose, the column order in carbon table is different from source table, use script "SELECT * FROM carbon table" to query, will get the column order similar as source table, rather than in carbon table's column order as expected.

CREATE TABLE IF NOT EXISTS carbon_table(
id String,
city String,
name String)
STORED AS carbondata;

INSERT INTO TABLE carbon_table SELECT * FROM source_table;

SELECT * FROM carbon_table;
id  city    name
1   jack    beijing
2   erlu    hangzhou
3   davi    shenzhen

As result shows, the second column is city in carbon table, but what inside is name, such as jack. This phenomenon is same with insert data into hive table.

If you want to insert data into corresponding column in carbon table, you have to specify the column order same in insert statement.

INSERT INTO TABLE carbon_table SELECT id, city, name FROM source_table;

Scenario 2 :

Insert operation will be failed when the number of column in carbon table is different from the column specified in select statement. The following insert operation will be failed.

INSERT INTO TABLE carbon_table SELECT id, city FROM source_table;

Scenario 3 :

When the column type in carbon table is different from the column specified in select statement. The insert operation will still success, but you may get NULL in result, because NULL will be substitute value when conversion type failed.

Why all executors are showing success in Spark UI even after Dataload command failed at Driver side?

Spark executor shows task as failed after the maximum number of retry attempts, but loading the data having bad records and BAD_RECORDS_ACTION (carbon.bad.records.action) is set as "FAIL" will attempt only once but will send the signal to driver as failed instead of throwing the exception to retry, as there is no point to retry if bad record found and BAD_RECORDS_ACTION is set to fail. Hence the Spark executor displays this one attempt as successful but the command has actually failed to execute. Task attempts or executor logs can be checked to observe the failure reason.

Why different time zone result for select query output when query SDK writer output?

SDK writer is an independent entity, hence SDK writer can generate carbondata files from a non-cluster machine that has different time zones. But at cluster when those files are read, it always takes cluster time-zone. Hence, the value of timestamp and date datatype fields are not original value. If wanted to control timezone of data while writing, then set cluster's time-zone in SDK writer by calling below API.

TimeZone.setDefault(timezoneValue)

Example:

cluster timezone is Asia/Shanghai
TimeZone.setDefault(TimeZone.getTimeZone("Asia/Shanghai"))

How to check LRU cache memory footprint?

To observe the LRU cache memory footprint in the logs, configure the below properties in log4j.properties file.

log4j.logger.org.apache.carbondata.core.cache.CarbonLRUCache = DEBUG

This property will enable the DEBUG log for the CarbonLRUCache and UnsafeMemoryManager which will print the information of memory consumed using which the LRU cache size can be decided. Note: Enabling the DEBUG log will degrade the query performance. Ensure carbon.max.driver.lru.cache.size is configured to observe the current cache size.

Example:

18/09/26 15:05:29 DEBUG CarbonLRUCache: main Required size for entry /home/target/store/default/stored_as_carbondata_table/Fact/Part0/Segment_0/0_1537954529044.carbonindexmerge :: 181 Current cache size :: 0
18/09/26 15:05:30 INFO CarbonLRUCache: main Removed entry from InMemory lru cache :: /home/target/store/default/stored_as_carbondata_table/Fact/Part0/Segment_0/0_1537954529044.carbonindexmerge

Note: If Removed entry from InMemory LRU cache are frequently observed in logs, you may have to increase the configured LRU size.

To observe the LRU cache from heap dump, check the heap used by CarbonLRUCache class.

How to deal with the trailing task in query?

When tuning query performance, user may found that a few tasks slow down the overall query progress. To improve performance in such case, user can set spark.locality.wait and spark.speculation=true to enable speculation in spark, which will launch multiple task and get the result the one of the task which is finished first. Besides, user can also consider following configurations to further improve performance in this case.

Example:

spark.locality.wait = 500
spark.speculation = true
spark.speculation.quantile = 0.75
spark.speculation.multiplier = 5
spark.blacklist.enabled = false

Note:

spark.locality control data locality the value of 500 is used to shorten the waiting time of spark.

spark.speculation is a group of configuration, that can monitor trailing tasks and start new tasks when conditions are met.

spark.blacklist.enabled, avoid reduction of available executors due to blacklist mechanism.

How to manage hybrid file format in carbondata table

Refer Heterogeneous format segments in carbondata

Getting tablestatus.lock issues When loading data

Symptom

17/11/11 16:48:13 ERROR LocalFileLock: main hdfs:/localhost:9000/carbon/store/default/hdfstable/tablestatus.lock (No such file or directory)
java.io.FileNotFoundException: hdfs:/localhost:9000/carbon/store/default/hdfstable/tablestatus.lock (No such file or directory)
	at java.io.FileOutputStream.open0(Native Method)
	at java.io.FileOutputStream.open(FileOutputStream.java:270)
	at java.io.FileOutputStream.<init>(FileOutputStream.java:213)
	at java.io.FileOutputStream.<init>(FileOutputStream.java:101)

Possible Cause If you use <hdfs path> as store path when creating carbonsession, may get the errors,because the default is LOCALLOCK.

Procedure Before creating carbonsession, sets as below:

import org.apache.carbondata.core.util.CarbonProperties
import org.apache.carbondata.core.constants.CarbonCommonConstants
CarbonProperties.getInstance().addProperty(CarbonCommonConstants.LOCK_TYPE, "HDFSLOCK")

Failed to load thrift libraries

Symptom

Thrift throws following exception :

thrift: error while loading shared libraries:
libthriftc.so.0: cannot open shared object file: No such file or directory

Possible Cause

The complete path to the directory containing the libraries is not configured correctly.

Procedure

Follow the Apache thrift docs at https://thrift.apache.org/docs/install to install thrift correctly.

Failed to launch the Spark Shell

Symptom

The shell prompts the following error :

org.apache.spark.sql.CarbonContext$$anon$$apache$spark$sql$catalyst$analysis
$OverrideCatalog$_setter_$org$apache$spark$sql$catalyst$analysis
$OverrideCatalog$$overrides_$e

Possible Cause

The Spark Version and the selected Spark Profile do not match.

Procedure

  1. Ensure your spark version and selected profile for spark are correct.

  2. Use the following command :

mvn -Pspark-2.4 -Dspark.version={yourSparkVersion} clean package

Note : Refrain from using "mvn clean package" without specifying the profile.

Failed to execute load query on cluster

Symptom

Load query failed with the following exception:

Dictionary file is locked for updation.

Possible Cause

The carbon.properties file is not identical in all the nodes of the cluster.

Procedure

Follow the steps to ensure the carbon.properties file is consistent across all the nodes:

  1. Copy the carbon.properties file from the master node to all the other nodes in the cluster. For example, you can use ssh to copy this file to all the nodes.

  2. For the changes to take effect, restart the Spark cluster.

Failed to execute insert query on cluster

Symptom

Load query failed with the following exception:

Dictionary file is locked for updation.

Possible Cause

The carbon.properties file is not identical in all the nodes of the cluster.

Procedure

Follow the steps to ensure the carbon.properties file is consistent across all the nodes:

  1. Copy the carbon.properties file from the master node to all the other nodes in the cluster. For example, you can use scp to copy this file to all the nodes.

  2. For the changes to take effect, restart the Spark cluster.

Failed to connect to hiveuser with thrift

Symptom

We get the following exception :

Cannot connect to hiveuser.

Possible Cause

The external process does not have permission to access.

Procedure

Ensure that the Hiveuser in mysql must allow its access to the external processes.

Failed to read the metastore db during table creation

Symptom

We get the following exception on trying to connect :

Cannot read the metastore db

Possible Cause

The metastore db is dysfunctional.

Procedure

Remove the metastore db from the carbon.metastore in the Spark Directory.

Failed to load data on the cluster

Symptom

Data loading fails with the following exception :

Data Load failure exception

Possible Cause

The following issue can cause the failure :

  1. The core-site.xml, hive-site.xml, yarn-site and carbon.properties are not consistent across all nodes of the cluster.

  2. Path to hdfs ddl is not configured correctly in the carbon.properties.

Procedure

Follow the steps to ensure the following configuration files are consistent across all the nodes:

  1. Copy the core-site.xml, hive-site.xml, yarn-site, carbon.properties files from the master node to all the other nodes in the cluster. For example, you can use scp to copy this file to all the nodes.

    Note : Set the path to hdfs ddl in carbon.properties in the master node.

  2. For the changes to take effect, restart the Spark cluster.

Failed to insert data on the cluster

Symptom

Insertion fails with the following exception :

Data Load failure exception

Possible Cause

The following issue can cause the failure :

  1. The core-site.xml, hive-site.xml, yarn-site and carbon.properties are not consistent across all nodes of the cluster.

  2. Path to hdfs ddl is not configured correctly in the carbon.properties.

Procedure

Follow the steps to ensure the following configuration files are consistent across all the nodes:

  1. Copy the core-site.xml, hive-site.xml, yarn-site, carbon.properties files from the master node to all the other nodes in the cluster. For example, you can use scp to copy this file to all the nodes.

    Note : Set the path to hdfs ddl in carbon.properties in the master node.

  2. For the changes to take effect, restart the Spark cluster.

Failed to execute Concurrent Operations on table by multiple workers

Symptom

Execution fails with the following exception :

Table is locked for updation.

Possible Cause

Concurrency not supported.

Procedure

Worker must wait for the query execution to complete and the table to release the lock for another query execution to succeed.

Failed to create index and drop index is also not working

Symptom

Execution fails with the following exception :

HDFS Quota Exceeded

Possible Cause

HDFS Quota is set, and it is not letting carbondata write or modify any files.

Procedure

Drop that particular index using Drop Index command so as to clear the stale folders.

How to recover Table status file if lost

When carbon.enable.multi.version.table.status property if enabled, table will have multiple versions of table status files in the metadata directory. If the current version file is lost, then user can run TableStatusRecovery tool to recover the table status file.

Example:

TableStatusRecovery.main(args) --> args is of length two: 1. Database Name 2. Table Name

Note:

TableStatus Recovery tool cannot recover table status version files for the below two scenarios

  1. After compaction, if table status file is lost, cannot recover compacted commit transaction, as the lost version file only has merged load details.
  2. After Delete segment by Id/Date, if table status file is lost, cannot recover deleted segment commit transaction, as the lost version file only has the segment status as deleted.
  3. Table status recovery on materialized view table is not supported.

Why deleted partition data still showing in file system

By default, the dropped partition data will not be physically removed from the table store until the table is dropped. Enable carbon.enable.partitiondata.trash property to move all the dropped partitions data to trash during alter table DROP PARTITION operation itself.