Cassandra Data Migrator
Use Cassandra Data Migrator to migrate and validate tables between Origin and Target Cassandra clusters, with available logging and reconciliation support.
Cassandra Data Migrator prerequisites
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Install or switch to Java 8. The Spark binaries are compiled with this version of Java.
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Install Spark 3.3.1 on a single VM (no cluster necessary) where you want to run this job.
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Optionally, install Maven 3.8.x, if you want to build the JAR for local development.
You can install Apache Spark by running the following commands:
wget https://archive.apache.org/dist/spark/spark-3.3.1/spark-3.3.1-bin-hadoop3.tgz
tar -xvzf spark-3.3.1-bin-hadoop3.tgzInstall Cassandra Data Migrator as a Container
Get the latest image that includes all dependencies from DockerHub.
All migration tools (cassandra-data-migrator + dsbulk + cqlsh) are available in the /assets/ folder of the container.
Install Cassandra Data Migrator as a JAR file
Download the latest JAR file from the Cassandra Data Migrator GitHub repo.
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Version 4.x of Cassandra Data Migrator is not backward-compatible with |
Build Cassandra Data Migrator JAR for local development (optional)
Optionally, you can build the Cassandra Data Migrator JAR for local development. (You’ll need Maven 3.8.x.)
Example:
cd ~/github
git clone git@github.com:datastax/cassandra-data-migrator.git
cd cassandra-data-migrator
mvn clean packageThe fat jar (cassandra-data-migrator-x.y.z.jar) file should be present now in the target folder.
Cassandra Data Migrator steps
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Configure for your environment the
cdm*.propertiesfile that’s provided in the Cassandra Data Migrator GitHub repo. The file can have any name. It does not need to becdm.propertiesorcdm-detailed.properties. In both versions, only the parameters that aren’t commented out will be processed by thespark-submitjob. Other parameter values use defaults or are ignored. See the descriptions and defaults in each file. Refer to:-
The simplified sample properties configuration, cdm.properties. This file contains only those parameters that are commonly configured.
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The complete sample properties configuration, cdm-detailed.properties, for the full set of configurable settings.
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Place the properties file that you elected to use and customize where it can be accessed while running the job via
spark-submit. -
Run the job using
spark-submitcommand:
./spark-submit --properties-file cdm.properties /
--conf spark.cdm.schema.origin.keyspaceTable="<keyspacename>.<tablename>" /
--master "local[*]" --driver-memory 25G --executor-memory 25G /
--class com.datastax.cdm.job.Migrate cassandra-data-migrator-x.y.z.jar &> logfile_name_$(date +%Y%m%d_%H_%M).txt
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Cassandra Data Migrator steps in validation mode
To run your migration job with Cassandra Data Migrator in data validation mode, use class option --class com.datastax.cdm.job.DiffData.
Example:
./spark-submit --properties-file cdm.properties /
--conf spark.cdm.schema.origin.keyspaceTable="<keyspacename>.<tablename>" /
--master "local[*]" --driver-memory 25G --executor-memory 25G /
--class com.datastax.cdm.job.DiffData cassandra-data-migrator-x.y.z.jar &> logfile_name_$(date +%Y%m%d_%H_%M).txtThe Cassandra Data Migrator validation job will report differences as ERROR entries in the log file.
Example:
23/04/06 08:43:06 ERROR DiffJobSession: Mismatch row found for key: [key3] Mismatch: Target Index: 1 Origin: valueC Target: value999)
23/04/06 08:43:06 ERROR DiffJobSession: Corrected mismatch row in target: [key3]
23/04/06 08:43:06 ERROR DiffJobSession: Missing target row found for key: [key2]
23/04/06 08:43:06 ERROR DiffJobSession: Inserted missing row in target: [key2]|
To get the list of missing or mismatched records, grep for all |
You can also run the Cassandra Data Migrator validation job in an AutoCorrect mode. This mode can:
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Add any missing records from Origin to Target.
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Update any mismatched records between Origin and Target; this action makes Target the same as Origin.
To enable or disable this feature, use one or both of the following settings in your *.properties configuration file.
spark.cdm.autocorrect.missing false|true
spark.cdm.autocorrect.mismatch false|true|
The Cassandra Data Migrator validation job will never delete records from Target. The job only adds or updates data on Target. |
Migrating or validating specific partition ranges
You can also use Cassandra Data Migrator to migrate or validate specific partition ranges, by using a partition-file with the name ./<keyspacename>.<tablename>_partitions.csv. Use the following format in the CSV file, in the current folder as input.
Example:
-507900353496146534,-107285462027022883
-506781526266485690,1506166634797362039
2637884402540451982,4638499294009575633
798869613692279889,8699484505161403540Each line in the CSV represents a partition-range (min,max).
Alternatively, you can also pass the partition-file via a command-line parameter. Example:
./spark-submit --properties-file cdm.properties /
--conf spark.cdm.schema.origin.keyspaceTable="<keyspacename>.<tablename>" /
--conf spark.cdm.tokenRange.partitionFile="/<path-to-file>/<csv-input-filename>" /
--master "local[*]" --driver-memory 25G --executor-memory 25G /
--class com.datastax.cdm.job.<Migrate|DiffData> cassandra-data-migrator-x.y.z.jar &> logfile_name_$(date +%Y%m%d_%H_%M).txtThis mode is specifically useful to processes a subset of partition-ranges that may have failed during a previous run.
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A file named |
Perform large-field guardrail violation checks
Use Cassandra Data Migrator to identify large fields from a table that may break your cluster guardrails. For example, Astra DB has a 10MB limit for a single large field. Specify --class com.datastax.cdm.job.GuardrailCheck on the command. Example:
./spark-submit --properties-file cdm.properties /
--conf spark.cdm.schema.origin.keyspaceTable="<keyspacename>.<tablename>" /
--conf spark.cdm.feature.guardrail.colSizeInKB=10000 /
--master "local[*]" --driver-memory 25G --executor-memory 25G /
--class com.datastax.cdm.job.GuardrailCheck cassandra-data-migrator-4.x.x.jar &> logfile_name_$(date +%Y%m%d_%H_%M).txtCassandra Data Migrator reference
Common connection parameters for Origin and Target
| Property | Default | Notes |
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Hostname/IP address of the cluster. May be a comma-separated list, and can follow the |
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Port number to use if not specified on |
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(Not set) |
Secure Connect Bundle, used to connect to an Astra DB database. Example: |
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Username (or |
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Password (or |
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Hostname/IP address of the cluster. May be a comma-separated list, and can follow the |
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Port number to use if not specified on |
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(Not set) |
Secure Connect Bundle, used to connect to an Astra DB database. Default is not set. Example if set: |
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Username (or |
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Password (or |
Origin schema parameters
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Required - the |
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Default is |
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Default is empty, meaning the names will be determined automatically if |
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Default is
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Default is empty, meaning the names will be determined automatically if |
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Default is empty. If column names are changed between Origin and Target, then this mapped list provides a mechanism to associate the two. The format is |
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For optimization reasons, Cassandra Data Migrator does not migrate TTL and writetime at the field-level. Instead, Cassandra Data Migrator finds the field with the highest TTL, and the field with the highest writetime within an Origin table row, and uses those values on the entire Target table row. |
Target schema parameter
| Property | Default | Notes |
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This parameter is commented out. It’s the |
Auto-correction parameters
Auto-correction parameters allow Cassandra Data Migrator to correct data differences found between Origin and Target when you run the DiffData program. Typically, these are run disabled (for "what if" migration testing), which will generate a list of data discrepancies. The reasons for these discrepancies can then be investigated, and if necessary the parameters below can be enabled.
For information about invoking DiffData in a Cassandra Data Migrator command, see Cassandra Data Migrator steps in validation mode in this topic.
| Property | Default | Notes | ||
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When |
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When
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Commented out. By default, Counter tables are not copied when missing, unless explicitly set. |
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Commented out. This CSV file is used as input, as well as output when applicable. If the file exists, only the partition ranges in this file will be migrated or validated. Similarly, if exceptions occur while migrating or validating, partition ranges with exceptions will be logged to this file. |
Performance and operations parameters
Performance and operations parameters that can affect migration throughput, error handling, and similar concerns.
| Property | Default | Notes |
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In standard operation, the full token range (-2^63 .. 2^63-1) is divided into a number of parts, which will be parallel-processed. You should aim for each part to comprise a total of ≈1-10GB of data to migrate. During initial testing, you may want this to be a small number (such as |
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When writing to Target, this comprises the number of records that will be put into an |
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Concurrent number of operations across all parallel threads from Origin. This value may be adjusted up (or down), depending on the amount of data and the processing capacity of the Origin cluster. |
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Concurrent number of operations across all parallel threads from Target. This may be adjusted up (or down), depending on the amount of data and the processing capacity of the Target cluster. |
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Commented out. Read consistency from Origin, and also from Target when records are read for comparison purposes. The consistency parameters may be one of: |
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Commented out. Write consistency to Target. The consistency parameters may be one of: |
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Commented out. Number of rows of processing after which a progress log entry will be made. |
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Commented out. This parameter affects the frequency of reads from Origin, and also the frequency of flushes to Target. |
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Commented out. Controls how many errors a thread may encounter during |
Transformation parameters
Parameters to perform schema transformations between Origin and Target.
By default, these parameters are commented out.
| Property | Default | Notes | ||
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Timestamp value in milliseconds.
Partition and clustering columns cannot have null values, but if these are added as part of a schema transformation between Origin and Target, it is possible that the Origin side is null. In this case, the |
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Default is 0 (disabled). Timestamp value in microseconds to use as the |
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Default is |
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Default is empty. A comma-separated list of additional codecs to enable.
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Configuration for |
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Default is |
Cassandra filter parameters
Cassandra filters are applied on the coordinator node. Note that, depending on the filter, the coordinator node may need to do a lot more work than is normal, notably because Cassandra Data Migrator specifies ALLOW FILTERING.
By default, these parameters are commented out.
| Property | Default | Notes |
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Default is |
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Default is |
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CQL added to the |
Java filter parameters
Java filters are applied on the client node. Data must be pulled from the Origin cluster and then filtered. However, this option may have a lower impact on the production cluster than Cassandra filters. Java filters put load onto the Cassandra Data Migrator processing node, by sending more data from Cassandra. Cassandra filters put load on the Cassandra nodes, notably because Cassandra Data Migrator specifies ALLOW FILTERING, which could cause the coordinator node to perform a lot more work.
By default, these parameters are commented out.
| Property | Default | Notes |
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Percent (between 1 and 100) of the token in each Split that will be migrated. This property is used to do a wide and random sampling of the data. The percentage value is applied to each split. Invalid percentages will be treated as 100. |
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The lowest (inclusive) writetime values to be migrated. Using the |
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The highest (inclusive) writetime values to be migrated. Maximum timestamp of the columns specified by |
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Filter rows based on matching a configured value. With |
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String value to use as comparison. Whitespace on the ends of |
Constant column feature parameters
The constant columns feature allows you to add constant columns to the target table.
If used, the spark.cdm.feature.constantColumns.names, spark.cdm.feature.constantColumns.types, and spark.cdm.feature.constantColumns.values lists must all be the same length.
By default, these parameters are commented out.
| Property | Default | Notes |
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A comma-separated list of column names, such as |
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A comma-separated list of column types. |
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A comma-separated list of hard-coded values. Each value should be provided as you would use on the |
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Defaults to comma, but can be any regex character that works with |
Explode map feature parameters
The explode map feature allows you convert an Origin table Map into multiple Target table records.
By default, these parameters are commented out.
| Property | Default | Notes |
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The name of the map column, such as |
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The name of the column on the Target table that will hold the map key, such as |
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The name of the column on the Target table that will hold the map value, such as |
Guardrail feature parameter
The guardrail feature manages records that exceed guardrail checks. The Guardrail job will generate a report; other jobs will skip records that exceed the guardrail limit.
By default, these parameters are commented out.
| Property | Default | Notes |
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The |
TLS (SSL) connection parameters
TLS (SSL) connection parameters, if configured, for Origin and Target. Note that a secure connect bundle (SCB) embeds these details.
By default, these parameters are commented out.
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If TLS is used, set to |
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Path to the Java truststore file. |
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Password needed to open the truststore. |
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`spark.cdm.connect.origin.tls.trustStore.type ` |
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Path to the Java keystore file. |
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Password needed to open the keystore. |
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If TLS is used, set to |
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Path to the Java truststore file. |
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Password needed to open the truststore. |
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`spark.cdm.connect.target.tls.trustStore.type ` |
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Path to the Java keystore file. |
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Password needed to open the keystore. |
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