Hadoop provides scalable data storage using the the Hadoop Distributed File System (HDFS) and and fast parallel data processing on a fault-tolerant cluster of computers. Learn more about about Hadoop.
See See Hadoop and Datameer to to learn more about Hadoop and how to use it with Datameer.
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General configuration
- Click the Admin the Admin tab.
- Click the the Hadoop Cluster tab tab at the left side. The current settings are shown.
- Click Edit to make changes.
- Click Click Save when when you are finished making changes.
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- Click the Admin tab.
- Click the Hadoop Cluster tab at the left side. The current settings are shown.
- Click Edit to make changes.
- Select Hadoop Cluster for the mode.
Specify the name node and add a private folder path or use impersonation if applicable.
Whitespaces aren't supported for use in file/folder paths. Avoid setting up Datameer storage directories (storage root path, temp paths, execution framework specific staging directories, etc.) with a whitespace in the path.Note Impersonation notes:
- There is one-to-one mapping between the Datameer user and the OS user.
- The OS user who is launching the Datameer process must be a sudoer.
- The temp folder for the Datameer installation local file system as well as in the Hadoop cluster (for Datameer) should have read/write access.<Datameer_Installation_Folder>/tmp
(Local FileSystem)<Datameer_Private_Folder>/temp
(Hadoop Cluster and MapR)
Learn about Enabling Secure Impersonation with Datameer.
- Specify YARN settings.
Use the properties text boxes to add Hadoop and custom
properties.
Enter a name and value to add a property, or delete a name and value pair to delete the propertyproperties.
Enter a name and value to add a property, or delete a name and value pair to delete the property.Note Within these edit fields, backslash (
\
) characters are interpreted by Datameer as an escape character rather than a plain text character. In order to produce the actual backslash character, you have to type two backslashes:Code Block language text example.property=example text, a backslash \\ and further text
The second backslash is needed as you are effectively editing a Java properties file in these edit fields.
- Logging options. Select the severity of messages to be logged. The logging customization field allows to record exactly what is needed.
- Click Save when you are finished making changes.
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Autoconfigure grid mode
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Warning |
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This feature is not supported with Cloudera Manager Safety Valve. |
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- Go to Admin tab > Hadoop Cluster.
- Select Autoconfigure Cluster in the Mode field.
- In the Hadoop Configuration Directory field, enter the directory where the configuration files for Hadoop are located.
- Enter the path to the folder where Datameer puts its private information in Hadoop's file system in the Datameer Private Folder field.
- Enter the number of concurrent jobs.
- Select whether to use secure impersonation.
- Edit the Hadoop or custom properties as necessary.
If theyarn.timeline-service.enabled
property is true in the Hadoop conf files, setyarn.timeline-service.enabled=false
as a custom property. (This change is not needed as of Datameer v6.1)
- Click Save.
To update your Datameer Autoconfig Grid Mode cluster configuration, click Edit on the Hadoop Cluster page and click Save. After saving has been completed, the updated configuration has been applied. New updates to the settings are not shown in the conductor.log but are displayed when clicking again on Edit for Autoconfigure Grid Mode under the Hadoop Properties label.
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- Click the Admin tab at the top of the page.
- Click the Hadoop Cluster tab at the left side. The current settings are shown.
- Click Edit to make changes and choose MapR in the mode list.
Add the cluster name, the Datameer private folder, and check the boxes if using Simple Impersonation for Datameer to submit jobs and access the HDFS on behalf of Datameer user, and the Max Concurrent jobs.
There is one-to-one mapping between the Datameer user and the OS user.
The OS user who is launching the Datameer process must be a sudoer.
The temp folder for the Datameer installation local file system as well as in the hadoop cluster (for Datameer) should have read/write access.<Datameer_Installation_Folder>/tmp
(Local FileSystem)<Datameer_Private_Folder>/temp
(Hadoop Cluster and MapR)
Note
Connecting to a secure MapR clusterAnchor secure_mapr secure_mapr 1) Obtain the MapR ticket for the user who is running the Datameer application. Execute the following command on the shell:
Code Block maprlogin password -user <user_who_starts_datameer>
2) Install Datameer and open
<Datameer_Home>/etc/das-env.sh
and add the following system property to the Java arguments:Code Block -Dmapr.secure.mode=true
3) Start and configure Datameer using MapR Grid Mode.
The option to connect using Secure Impersonation is now available.
4) (Optional) If there is a failure in saving the configuration:
Code Block Caused by: java.io.IOException: Can't get Master Kerberos principal for use as renewer
Add the following custom Hadoop properties under the Hadoop Admin page:
Code Block yarn.resourcemanager.principal=<value>
The value for this property can be found in the
yarn-site.xml
file in your Hadoop cluster configuration.The steps to achieve impersonation are same as for a secured Kerberos cluster.
- If required, enter properties. Enter a name and value to add a property, or delete a name and value pair to delete that property.
- Logging options. Select the severity of messages to be logged. It is also possible to write custom log settings to record exactly what is needed.
- Click Save when you are finished making changes.
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- Specify the NameService to which the Datameer instance should be working with in HDFS NameNode text box: hdfs://nameservice1
Specify the following Hadoop properties in Custom Property field:
Code Block language bash title Custom Properties ### HDFS Name Node (NN) High Availability (HA) ### dfs.nameservices=nameservice1 dfs.ha.namenodes.nameservice1=nn1,nn2 dfs.namenode.rpc-address.nameservice1.nn1=<server-address>:8020 dfs.namenode.rpc-address.nameservice1.nn2=<server-address>:8020 dfs.client.failover.proxy.provider.nameservice1=org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider
If HDFS HA is configured for automatic failover by using Quorum Journal Manager (QJM), you need to add the following additional custom properties:
Code Block ### HDFS HA Autotmatic Failover # By using the Quorum Journal Manager (QJM) dfs.ha.automatic-failover.enabled=true ha.zookeeper.quorum=<zookeepperHost1>.<domain>.<tld>:2181,<zookeepperHostn>.<domain>.<tld>:2181
Check the current NameNode (NN) setting within the database:
Code Block mysql -udap -pdap dap -Bse "SELECT uri FROM data" | cut -d"/" -f3 | sort | uniq
The command above should have only one result, the former <host>.<domain>.<tld>:<port> value configured under Admin tab tab > Hadoop Cluster > > Storage Settings > > HDFS NameNode.
Update the paths to the new location in the Datameer DB:
Code Block ./bin/update_paths.sh hdfs://<old.namenode>:8020/<root-path> hdfs://nameservice1/<root-path>
Check to ensure the new NameNode has been applied to the database and that the path is correct:
Code Block mysql -udap -pdap dap -Bse "SELECT uri FROM data" | cut -d"/" -f3,4,5 | sort | uni
The command above has only one result, the virtual NameNode value including the path configured under Admin tab tab > Hadoop Cluster > Storage Settings > Datameer Private Folder.
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Specify the resource manager with which the Datameer instance should be working in Yarn Resource Manager Address field: yarnRM.
YARN Application Classpath is a comma-separated list of CLASSPATH entries.
Specify the following Hadoop properties in Custom Property field:
Code Block language bash title Custom Properties ### Resource Manager (RM) YARN High Availability (HA) ### yarn.resourcemanager.cluster-id=yarnRM yarn.resourcemanager.ha.enabled=true yarn.resourcemanager.ha.rm-ids=rm1,rm2 yarn.resourcemanager.recovery.enabled=true yarn.resourcemanager.store.class=org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore yarn.resourcemanager.zk-address=<server-adress>:2181 ## RM1 ## yarn.resourcemanager.hostname.rm1=<server> yarn.resourcemanager.address.rm1=<server-adress>:8032 yarn.resourcemanager.scheduler.address.rm1=<server-adress>:8030 yarn.resourcemanager.webapp.address.rm1=<server-adress>:8088 yarn.resourcemanager.resource-tracker.address.rm1=<server-adress>:8031 ## RM2 ## yarn.resourcemanager.hostname.rm2=<server> yarn.resourcemanager.address.rm2=<server-adress>:8032 yarn.resourcemanager.scheduler.address.rm2=<server-adress>:8030 yarn.resourcemanager.webapp.address.rm2=<server-adress>:8088 yarn.resourcemanager.resource-tracker.address.rm2=<server-adress>:8031
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Custom properties consist of a name (key) and value pair separated with a '='. These properties are used to configure Hadoop jobs.
For example you can specify the output compression codec for jobs by entering entering mapred.output.compression.codec=org.apache.hadoop.io.compress.DefaultCodec
into into the custom property field.
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There are some additional Datameer specific properties as well. These are:
Name | Default value | Description | Location | Since |
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| -1 (unlimited) | The maximum number of errors that should be written per MR task. |
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| 1 | The replication factor for the error-logs which are getting written for dropped records |
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| 100 | Maximum array size that a function may return for previews. If this value is exceeded the excess records are chopped off. This is only used on the Datameer server to keep the previews small. |
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| 52428800 (50MB) | Maximum size of memory which is used to cache values before falling back to file. This property is used in self joins and from the join strategies REDUCE_SIDE and PRE_SORT_MAP_SIDE |
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| 15728640 (15MB) | Maximum size for the smaller join file of a join. Datameer uses different join strategies. The fastest join strategy is when one input of the two fits into memory. This property marks the upper bound when a file, depending on its size, is assumed to fit into memory. Note that depending on compression and record-structure a 15 MB file on disk can easily blow up to 500 MB in memory. |
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| empty | A comma separated list of Join strategies which should explicitly not be used. Available are MEMORY_BACKED_MAP_SIDE, REDUCE_SIDE and PRE_SORT_MAP_SIDE |
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| datameer.dap.common.util.ZipCodec | Additional compression codecs that are added to io.compression.codecs |
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| Datameer merges multiple small files into combined splits and splits large files into multiple smaller splits. Setting this property can control the maximum number of splits that should be created. This is good to reduce the number of file handlers due to a lack of RPC resources. | |||
| Sets the minimum number of splits that should be created. This could be used in increase the number of map-tasks for smaller jobs to better utilize the cluster. | |||
| 50 | Sets the JDBC batch size for export jobs. Helpful on debugging export issues during JDBC batch commit. | ||
| TRANSACTION_READ_COMMITTED | The jdbc transaction-level which should be used for imports from database. You can choose between TRANSACTION_NONE, TRANSACTION_READ_UNCOMMITTED, TRANSACTION_READ_COMMITTED, TRANSACTION_REPEATABLE_READ and TRANSACTION_SERIALIZABLE which corresponds to the transaction-isolation levels from java.sql.Connection. |
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das.debug.job | false | Shortcut property for enabling a set of debug related properties for map-reduce jobs like periodic thread-dumps, periodic counter dumps, task-log retrieval, etc.. | 1.4.8 | |
| (-1=disabled, >0 enabled) | A thread dump is taken every x ms and logged to stdout (so it ends in the task-logs). | ||
| (-1=disabled, >0 enabled) |
A hadoop-counter dump is taken every x ms and logged to stdout (so it ends in the task-logs). | 1.4.8 | |||
| 20 | Number of task-logs which getting copied into job-artifacts folder when job completes. | ||
| false | When true, copies the task-logs into job-artifacts folder even when job completes successfully. | ||
das.sampling.lookahead.maxdepth | 5 | Smart sampling uses information from the downstream sheets in a workbook while sampling data for the preview of a sheet. This parameter sets the number of downstream levels which are considered. | 2.0 | |
das.job.trigger.type | Tells who/what triggered a job. (USER, SCHEDULER, RESTAPI, IMPORTJOB, EXPORTJOB, WORKBOOK) | 3.1 | ||
| The name of the owner for each file and folder that will be created during a job run in HDFS | 3.1 | ||
das.job.hdfs.groupname | The name of the group for each file and folder that will be created during a job run in HDFS | 3.1 | ||
| The name of the user thats executes the job. | 3.1 | ||
| The name of the user thats the owns the configuration of the job. | 3.1 | ||
das.map-tasks-per-node | 10 | Set the number of map tasks per node. | 5.0 | |
das.reduce-tasks-per-node | 7 | Set the number of reduce tasks per node | 5.0 |
Custom properties exclusive for Datameer Smart Execution
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Available as of 5.0 with Smart Execution license |
Name | Default value | Description | Location | Since |
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tez.am.shuffle-vertex-manager.desired-task-input-size | 50 | Set the input size for each TEZ sub-task | das-common.properties | 5.0 |
| smart | Force a job to be run with a specific execution framework |
Standard MapReduce = MapReduce
Spark Smart Execution = Smart Local Mode = local (default and the only execution framework in local mode) | 5.0 | |||
das.execution-framework.small-job.max-records | 1000000 | Set a threshold size for number of records to choose execution framework | 5.0 | |
das.execution-framework.small-job.max-uncompressed | 1000000000 | Set a threshold size for uncompressed file size to choose execution framework | 5.0 |
Configuring Number of Hadoop Cluster Nodes
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- Add all available vcores from both head and worker nodes.
- Divide total number of vcores by the number of nodes in the cluster. This average constitutes the size of a single node. (If the node-based license allows 3 nodes, 3 times the average number of vcores can be used.)
Update the YARN scheduler on the Hadoop cluster to the number of specified vcores.
Code Block yarn.scheduler.capacity.<queue_name>.maximum-allocation-vcores=12
Add a a custom Hadoop property in Datameer to to recognize the number of available vcores.
For Tez:
Code Block tez.queue.name=<queue_name>
For Spark:
Code Block spark.yarn.queue=<queue_name>
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Datameer's <datameer-install-path>/tmp
folder folder stores temporary files required for Datameer.
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tmp/tez-plugin-jars
If the /tmp
folder folder is consuming too much space and other files are present in the folder, Datameer suggests it is safe to purge files that are older than three days.
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Datameer's Distributed File System cache cache <datameer-install-path>cache/dfscache
is is used for or caching data link and import job sample records as well as log and jdbc files.
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<cache name="dfsCache" maxEntriesLocalHeap="10000" timeToIdleSeconds="60" timeToLiveSeconds="60" eternal="false" diskPersistent="false" overflowToDisk="false" > |
This This dfscache
grows grows in size as the number of sample records from import jobs and data links is increased. Follow the troubleshooting guide if the Datameer filesystem is full due to excessive reduce-part-#### files generated in in dfsCache
folder folder.
The The dfscache
folder folder and its contents can be moved to a different location if needed by following the guide in our knowledge base.
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Define the time zone that should be used for displaying the date in the UI and for parsing date strings that do not specify a timezone. Use Use default
as as the value to use the local server's time zone.
In In conf/default.properties
you you can change the value designating the time zone:
system.property.das.default-timezone=default |
If the time zone is changed on the machine where Datameer is running, Datameer must be restarted to show the new default time zone configuration.
Examples
Time zone | Description |
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default | Local server time |
PST | Pacific Standard Time |
PST8PDT | This time zone changes to daylight saving time (DST) in the spring. The GMT offset is UTC/GMT -7 hours (PDT) during this time. In the fall it changes back to standard time, the GMT offset is then UTC/GMT -8 hours (PST). |
CST | Central Standard Time |
America/Los_Angeles | Time zone for Los Angeles (USA), this time zone changes to daylight saving time (DST) in the spring. The GMT offset is UTC/GMT -7 hours during this time. In the fall it changes back to standard time, the GMT offset is then UTC/GMT -8 hours. |
EST5EDT | This time zone changes to daylight saving time (DST) in the spring. The GMT offset is UTC/GMT -4 hours (EDT) during this time. In the fall it changes back to standard time, the GMT offset is then UTC/GMT -5 hours (EST). |