Oracle jdbc connector jar file download

Java DB was a rebranding of Apache Derby. Note that if you are using another DBMS, you might have to alter the code of the tutorial samples. There are many possible implementations of JDBC drivers. These implementations are categorized as follows:. Drivers of this type are generally dependent on a native library, which limits their portability.

It is not supported by Oracle. Type 2 : Drivers that are written partly in the Java programming language and partly in native code. These drivers use a native client library specific to the data source to which they connect.

Again, because of the native code, their portability is limited. Type 3 : Drivers that use a pure Java client and communicate with a middleware server using a database-independent protocol. The middleware server then communicates the client's requests to the data source.

Type 4 : Drivers that are pure Java and implement the network protocol for a specific data source. The client connects directly to the data source. Check which driver types comes with your DBMS. Installing a JDBC driver generally consists of copying the driver to your computer, then adding the location of it to your class path. No other special configuration is usually needed.

Go to the following link to download Apache Ant:. Ensure that the Apache Ant executable file is in your PATH environment variable so that you can run it from any directory. This should include a comma-delimited list of columns to import. Only rows where the id column has a value greater than will be imported. In some cases this query is not the most optimal so you can specify any arbitrary query returning two numeric columns using --boundary-query argument.

Sqoop can also import the result set of an arbitrary SQL query. Instead of using the --table , --columns and --where arguments, you can specify a SQL statement with the --query argument. When importing a free-form query, you must specify a destination directory with --target-dir.

If you want to import the results of a query in parallel, then each map task will need to execute a copy of the query, with results partitioned by bounding conditions inferred by Sqoop.

You must also select a splitting column with --split-by. Alternately, the query can be executed once and imported serially, by specifying a single map task with -m 1 :. The facility of using free-form query in the current version of Sqoop is limited to simple queries where there are no ambiguous projections and no OR conditions in the WHERE clause.

Use of complex queries such as queries that have sub-queries or joins leading to ambiguous projections can lead to unexpected results. Sqoop imports data in parallel from most database sources. You can specify the number of map tasks parallel processes to use to perform the import by using the -m or --num-mappers argument.

Each of these arguments takes an integer value which corresponds to the degree of parallelism to employ. By default, four tasks are used. Some databases may see improved performance by increasing this value to 8 or Do not increase the degree of parallelism greater than that available within your MapReduce cluster; tasks will run serially and will likely increase the amount of time required to perform the import.

Likewise, do not increase the degree of parallism higher than that which your database can reasonably support. Connecting concurrent clients to your database may increase the load on the database server to a point where performance suffers as a result. When performing parallel imports, Sqoop needs a criterion by which it can split the workload. Sqoop uses a splitting column to split the workload. By default, Sqoop will identify the primary key column if present in a table and use it as the splitting column.

The low and high values for the splitting column are retrieved from the database, and the map tasks operate on evenly-sized components of the total range. If the actual values for the primary key are not uniformly distributed across its range, then this can result in unbalanced tasks.

You should explicitly choose a different column with the --split-by argument. Sqoop cannot currently split on multi-column indices. If your table has no index column, or has a multi-column key, then you must also manually choose a splitting column. The option --autoreset-to-one-mapper is typically used with the import-all-tables tool to automatically handle tables without a primary key in a schema. When launched by Oozie this is unnecessary since Oozie use its own Sqoop share lib which keeps Sqoop dependencies in the distributed cache.

Oozie will do the localization on each worker node for the Sqoop dependencies only once during the first Sqoop job and reuse the jars on worker node for subsquencial jobs. By default, the import process will use JDBC which provides a reasonable cross-vendor import channel. Some databases can perform imports in a more high-performance fashion by using database-specific data movement tools.

By supplying the --direct argument, you are specifying that Sqoop should attempt the direct import channel. This channel may be higher performance than using JDBC. By default, Sqoop will import a table named foo to a directory named foo inside your home directory in HDFS.

You can adjust the parent directory of the import with the --warehouse-dir argument. When using direct mode, you can specify additional arguments which should be passed to the underlying tool. If the argument -- is given on the command-line, then subsequent arguments are sent directly to the underlying tool. For example, the following adjusts the character set used by mysqldump :. By default, imports go to a new target location. If you use the --append argument, Sqoop will import data to a temporary directory and then rename the files into the normal target directory in a manner that does not conflict with existing filenames in that directory.

By default, Sqoop uses the read committed transaction isolation in the mappers to import data. This may not be the ideal in all ETL workflows and it may desired to reduce the isolation guarantees. The --relaxed-isolation option can be used to instruct Sqoop to use read uncommitted isolation level. The read-uncommitted isolation level is not supported on all databases for example, Oracle , so specifying the option --relaxed-isolation may not be supported on all databases.

However the default mapping might not be suitable for everyone and might be overridden by --map-column-java for changing mapping to Java or --map-column-hive for changing Hive mapping. Sqoop provides an incremental import mode which can be used to retrieve only rows newer than some previously-imported set of rows. Sqoop supports two types of incremental imports: append and lastmodified. You can use the --incremental argument to specify the type of incremental import to perform.

You should specify append mode when importing a table where new rows are continually being added with increasing row id values. Sqoop imports rows where the check column has a value greater than the one specified with --last-value. An alternate table update strategy supported by Sqoop is called lastmodified mode. You should use this when rows of the source table may be updated, and each such update will set the value of a last-modified column to the current timestamp.

Rows where the check column holds a timestamp more recent than the timestamp specified with --last-value are imported. At the end of an incremental import, the value which should be specified as --last-value for a subsequent import is printed to the screen. When running a subsequent import, you should specify --last-value in this way to ensure you import only the new or updated data. This is handled automatically by creating an incremental import as a saved job, which is the preferred mechanism for performing a recurring incremental import.

See the section on saved jobs later in this document for more information. Delimited text is the default import format. You can also specify it explicitly by using the --as-textfile argument. This argument will write string-based representations of each record to the output files, with delimiter characters between individual columns and rows. These delimiters may be commas, tabs, or other characters. The delimiters can be selected; see "Output line formatting arguments.

Delimited text is appropriate for most non-binary data types. It also readily supports further manipulation by other tools, such as Hive. SequenceFiles are a binary format that store individual records in custom record-specific data types. These data types are manifested as Java classes. Sqoop will automatically generate these data types for you.

This format supports exact storage of all data in binary representations, and is appropriate for storing binary data for example, VARBINARY columns , or data that will be principly manipulated by custom MapReduce programs reading from SequenceFiles is higher-performance than reading from text files, as records do not need to be parsed. Avro data files are a compact, efficient binary format that provides interoperability with applications written in other programming languages. Avro also supports versioning, so that when, e.

By default, data is not compressed. You can compress your data by using the deflate gzip algorithm with the -z or --compress argument, or specify any Hadoop compression codec using the --compression-codec argument. This applies to SequenceFile, text, and Avro files. If this data is truly large, then these columns should not be fully materialized in memory for manipulation, as most columns are.

Instead, their data is handled in a streaming fashion. Large objects can be stored inline with the rest of the data, in which case they are fully materialized in memory on every access, or they can be stored in a secondary storage file linked to the primary data storage. By default, large objects less than 16 MB in size are stored inline with the rest of the data. The size at which lobs spill into separate files is controlled by the --inline-lob-limit argument, which takes a parameter specifying the largest lob size to keep inline, in bytes.

If you set the inline LOB limit to 0, all large objects will be placed in external storage. When importing to delimited files, the choice of delimiter is important. Delimiters which appear inside string-based fields may cause ambiguous parsing of the imported data by subsequent analysis passes.

For example, the string "Hello, pleased to meet you" should not be imported with the end-of-field delimiter set to a comma. Supported escape characters are:. For unambiguous parsing, both must be enabled. For example, via --mysql-delimiters. If unambiguous delimiters cannot be presented, then use enclosing and escaping characters.

The combination of optional enclosing and escaping characters will allow unambiguous parsing of lines. For example, suppose one column of a dataset contained the following values:. Note that to prevent the shell from mangling the enclosing character, we have enclosed that argument itself in single-quotes.

Here the imported strings are shown in the context of additional columns "1","2","3" , etc. The enclosing character is only strictly necessary when delimiter characters appear in the imported text. The enclosing character can therefore be specified as optional:. Even though Hive supports escaping characters, it does not handle escaping of new-line character.

Also, it does not support the notion of enclosing characters that may include field delimiters in the enclosed string. The --mysql-delimiters argument is a shorthand argument which uses the default delimiters for the mysqldump program. If you use the mysqldump delimiters in conjunction with a direct-mode import with --direct , very fast imports can be achieved.

While the choice of delimiters is most important for a text-mode import, it is still relevant if you import to SequenceFiles with --as-sequencefile. The generated class' toString method will use the delimiters you specify, so subsequent formatting of the output data will rely on the delimiters you choose. When Sqoop imports data to HDFS, it generates a Java class which can reinterpret the text files that it creates when doing a delimited-format import.

The delimiters are chosen with arguments such as --fields-terminated-by ; this controls both how the data is written to disk, and how the generated parse method reinterprets this data. The delimiters used by the parse method can be chosen independently of the output arguments, by using --input-fields-terminated-by , and so on. This is useful, for example, to generate classes which can parse records created with one set of delimiters, and emit the records to a different set of files using a separate set of delimiters.

Importing data into Hive is as simple as adding the --hive-import option to your Sqoop command line. If the Hive table already exists, you can specify the --hive-overwrite option to indicate that existing table in hive must be replaced. The script will be executed by calling the installed copy of hive on the machine where Sqoop is run.

This function is incompatible with --as-avrodatafile and --as-sequencefile. If you do use --escaped-by , --enclosed-by , or --optionally-enclosed-by when importing data into Hive, Sqoop will print a warning message. You can use the --hive-drop-import-delims option to drop those characters on import to give Hive-compatible text data.

Alternatively, you can use the --hive-delims-replacement option to replace those characters with a user-defined string on import to give Hive-compatible text data. Sqoop will pass the field and record delimiters through to Hive. Sqoop will by default import NULL values as string null. You should append parameters --null-string and --null-non-string in case of import job or --input-null-string and --input-null-non-string in case of an export job if you wish to properly preserve NULL values.

The table name used in Hive is, by default, the same as that of the source table. You can control the output table name with the --hive-table option. Hive can put data into partitions for more efficient query performance. You can tell a Sqoop job to import data for Hive into a particular partition by specifying the --hive-partition-key and --hive-partition-value arguments. The partition value must be a string. Please see the Hive documentation for more details on partitioning. You can import compressed tables into Hive using the --compress and --compression-codec options.

One downside to compressing tables imported into Hive is that many codecs cannot be split for processing by parallel map tasks. The lzop codec, however, does support splitting. When importing tables with this codec, Sqoop will automatically index the files for splitting and configuring a new Hive table with the correct InputFormat. This feature currently requires that all partitions of a table be compressed with the lzop codec. Sqoop can also import records into a table in HBase.

Sqoop will import data to the table specified as the argument to --hbase-table. Each row of the input table will be transformed into an HBase Put operation to a row of the output table. The key for each row is taken from a column of the input.

By default Sqoop will use the split-by column as the row key column. If that is not specified, it will try to identify the primary key column, if any, of the source table.

You can manually specify the row key column with --hbase-row-key. Each output column will be placed in the same column family, which must be specified with --column-family. This function is incompatible with direct import parameter --direct. If the input table has composite key, the --hbase-row-key must be in the form of a comma-separated list of composite key attributes.

In this case, the row key for HBase row will be generated by combining values of composite key attributes using underscore as a separator. NOTE: Sqoop import for a table with composite key will work only if parameter --hbase-row-key has been specified. If the target table and column family do not exist, the Sqoop job will exit with an error. You should create the target table and column family before running an import. If you specify --hbase-create-table , Sqoop will create the target table and column family if they do not exist, using the default parameters from your HBase configuration.

Sqoop currently serializes all values to HBase by converting each field to its string representation as if you were importing to HDFS in text mode , and then inserts the UTF-8 bytes of this string in the target cell. Sqoop will skip all rows containing null values in all columns except the row key column. To decrease the load on hbase, Sqoop can do bulk loading as opposed to direct writes. To use bulk loading, enable it using --hbase-bulkload. Sqoop will import data to the table specified as the argument to --accumulo-table.

Each row of the input table will be transformed into an Accumulo Mutation operation to a row of the output table. You can manually specify the row key column with --accumulo-row-key.

Each output column will be placed in the same column family, which must be specified with --accumulo-column-family. This function is incompatible with direct import parameter --direct , and cannot be used in the same operation as an HBase import. If the target table does not exist, the Sqoop job will exit with an error, unless the --accumulo-create-table parameter is specified.

Otherwise, you should create the target table before running an import. Sqoop currently serializes all values to Accumulo by converting each field to its string representation as if you were importing to HDFS in text mode , and then inserts the UTF-8 bytes of this string in the target cell. By default, no visibility is applied to the resulting cells in Accumulo, so the data will be visible to any Accumulo user. Use the --accumulo-visibility parameter to specify a visibility token to apply to all rows in the import job.

In order to connect to an Accumulo instance, you must specify the location of a Zookeeper ensemble using the --accumulo-zookeepers parameter, the name of the Accumulo instance --accumulo-instance , and the username and password to connect with --accumulo-user and --accumulo-password respectively.

As mentioned earlier, a byproduct of importing a table to HDFS is a class which can manipulate the imported data. Therefore, you should use this class in your subsequent MapReduce processing of the data. The class is typically named after the table; a table named foo will generate a class named foo. You may want to override this class name. Similarly, you can specify just the package name with --package-name.

The following import generates a class named com. SomeTable :. You can control the output directory with --outdir. The import process compiles the source into. You can select an alternate target directory with --bindir. If you already have a compiled class that can be used to perform the import and want to suppress the code-generation aspect of the import process, you can use an existing jar and class by providing the --jar-file and --class-name options.

This command will load the SomeTableType class out of mydatatypes. Properties can be specified the same as in Hadoop configuration files, for example:. Storing data in SequenceFiles, and setting the generated class name to com. Employee :. Performing an incremental import of new data, after having already imported the first , rows of a table:. Data from each table is stored in a separate directory in HDFS.

For the import-all-tables tool to be useful, the following conditions must be met:. Although the Hadoop generic arguments must preceed any import arguments, the import arguments can be entered in any order with respect to one another. These arguments behave in the same manner as they do when used for the sqoop-import tool, but the --table , --split-by , --columns , and --where arguments are invalid for sqoop-import-all-tables.

The import-all-tables tool does not support the --class-name argument. You may, however, specify a package with --package-name in which all generated classes will be placed. A PDS is akin to a directory on the open systems. The records in a dataset can contain only character data.

Records will be stored with the entire record as a single text field. Sqoop is designed to import mainframe datasets into HDFS. To do so, you must specify a mainframe host name in the Sqoop --connect argument. You might need to authenticate against the mainframe host to access it. You can use the --username to supply a username to the mainframe. Sqoop provides couple of different ways to supply a password, secure and non-secure, to the mainframe which is detailed below.

Secure way of supplying password to the mainframe. You can use the --dataset argument to specify a partitioned dataset name. All sequential datasets in the partitioned dataset will be imported. Sqoop imports data in parallel by making multiple ftp connections to the mainframe to transfer multiple files simultaneously. You can adjust this value to maximize the data transfer rate from the mainframe. By default, Sqoop will import all sequential files in a partitioned dataset pds to a directory named pds inside your home directory in HDFS.

By default, each record in a dataset is stored as a text record with a newline at the end. Since mainframe record contains only one field, importing to delimited files will not contain any field delimiter.

However, the field may be enclosed with enclosing character or escaped by an escaping character. You should use this class in your subsequent MapReduce processing of the data.

The class is typically named after the partitioned dataset name; a partitioned dataset named foo will generate a class named foo.

SomePDS :. The target table must already exist in the database. The input files are read and parsed into a set of records according to the user-specified delimiters. The default operation is to transform these into a set of INSERT statements that inject the records into the database.

In "update mode," Sqoop will generate UPDATE statements that replace existing records in the database, and in "call mode" Sqoop will make a stored procedure call for each record. Although the Hadoop generic arguments must preceed any export arguments, the export arguments can be entered in any order with respect to one another. Table The --export-dir argument and one of --table or --call are required. These specify the table to populate in the database or the stored procedure to call , and the directory in HDFS that contains the source data.

By default, all columns within a table are selected for export. This should include a comma-delimited list of columns to export. For example: --columns "col1,col2,col3". Note that columns that are not included in the --columns parameter need to have either defined default value or allow NULL values. Otherwise your database will reject the imported data which in turn will make Sqoop job fail. You can control the number of mappers independently from the number of files present in the directory.

Export performance depends on the degree of parallelism. By default, Sqoop will use four tasks in parallel for the export process.

This may not be optimal; you will need to experiment with your own particular setup. Additional tasks may offer better concurrency, but if the database is already bottlenecked on updating indices, invoking triggers, and so on, then additional load may decrease performance. The --num-mappers or -m arguments control the number of map tasks, which is the degree of parallelism used. Some databases provides a direct mode for exports as well. Use the --direct argument to specify this codepath.

This may be higher-performance than the standard JDBC codepath. The --input-null-string and --input-null-non-string arguments are optional. If --input-null-string is not specified, then the string "null" will be interpreted as null for string-type columns. If --input-null-non-string is not specified, then both the string "null" and the empty string will be interpreted as null for non-string columns.

Note that, the empty string will be always interpreted as null for non-string columns, in addition to other string if specified by --input-null-non-string. Since Sqoop breaks down export process into multiple transactions, it is possible that a failed export job may result in partial data being committed to the database. This can further lead to subsequent jobs failing due to insert collisions in some cases, or lead to duplicated data in others. You can overcome this problem by specifying a staging table via the --staging-table option which acts as an auxiliary table that is used to stage exported data.

The staged data is finally moved to the destination table in a single transaction. In order to use the staging facility, you must create the staging table prior to running the export job. This table must be structurally identical to the target table. This table should either be empty before the export job runs, or the --clear-staging-table option must be specified. If the staging table contains data and the --clear-staging-table option is specified, Sqoop will delete all of the data before starting the export job.

Support for staging data prior to pushing it into the destination table is not always available for --direct exports.

It is also not available when export is invoked using the --update-key option for updating existing data, and when stored procedures are used to insert the data. By default, sqoop-export appends new rows to a table; each input record is transformed into an INSERT statement that adds a row to the target database table. If your table has constraints e. This mode is primarily intended for exporting records to a new, empty table intended to receive these results. If you specify the --update-key argument, Sqoop will instead modify an existing dataset in the database.

The row a statement modifies is determined by the column name s specified with --update-key. For example, consider the following table definition:. In effect, this means that an update-based export will not insert new rows into the database. Likewise, if the column specified with --update-key does not uniquely identify rows and multiple rows are updated by a single statement, this condition is also undetected.

The argument --update-key can also be given a comma separated list of column names. In which case, Sqoop will match all keys from this list before updating any existing record. Depending on the target database, you may also specify the --update-mode argument with allowinsert mode if you want to update rows if they exist in the database already or insert rows if they do not exist yet.

Sqoop automatically generates code to parse and interpret records of the files containing the data to be exported back to the database. If these files were created with non-default delimiters comma-separated fields with newline-separated records , you should specify the same delimiters again so that Sqoop can parse your files.

If you specify incorrect delimiters, Sqoop will fail to find enough columns per line. This will cause export map tasks to fail by throwing ParseExceptions. If the records to be exported were generated as the result of a previous import, then the original generated class can be used to read the data back.

Specifying --jar-file and --class-name obviate the need to specify delimiters in this case. The use of existing generated code is incompatible with --update-key ; an update-mode export requires new code generation to perform the update.

You cannot use --jar-file , and must fully specify any non-default delimiters. Exports are performed by multiple writers in parallel. Each writer uses a separate connection to the database; these have separate transactions from one another. Every statements, the current transaction within a writer task is committed, causing a commit every 10, rows.

This ensures that transaction buffers do not grow without bound, and cause out-of-memory conditions. Therefore, an export is not an atomic process. Partial results from the export will become visible before the export is complete. If an export map task fails due to these or other reasons, it will cause the export job to fail. The results of a failed export are undefined. Each export map task operates in a separate transaction.

Furthermore, individual map tasks commit their current transaction periodically. If a task fails, the current transaction will be rolled back.

Any previously-committed transactions will remain durable in the database, leading to a partially-complete export. If Sqoop attempts to insert rows which violate constraints in the database for example, a particular primary key value already exists , then the export fails. Alternatively, you can specify the columns to be exported by providing --columns "col1,col2,col3". Please note that columns that are not included in the --columns parameter need to have either defined default value or allow NULL values.

Another basic export to populate a table named bar with validation enabled: More Details. Validate the data copied, either import or export by comparing the row counts from the source and the target post copy. There are 3 basic interfaces: ValidationThreshold - Determines if the error margin between the source and target are acceptable: Absolute, Percentage Tolerant, etc. Default implementation is AbsoluteValidationThreshold which ensures the row counts from source and targets are the same.

Default implementation is LogOnFailureHandler that logs a warning message to the configured logger. Validator - Drives the validation logic by delegating the decision to ValidationThreshold and delegating failure handling to ValidationFailureHandler. The default implementation is RowCountValidator which validates the row counts from source and the target.

The validation framework is extensible and pluggable. It comes with default implementations but the interfaces can be extended to allow custom implementations by passing them as part of the command line arguments as described below. Validation currently only validates data copied from a single table into HDFS. The following are the limitations in the current implementation:.

A basic export to populate a table named bar with validation enabled:. Imports and exports can be repeatedly performed by issuing the same command multiple times. Especially when using the incremental import capability, this is an expected scenario. Sqoop allows you to define saved jobs which make this process easier. A saved job records the configuration information required to execute a Sqoop command at a later time.

The section on the sqoop-job tool describes how to create and work with saved jobs. You can configure Sqoop to instead use a shared metastore , which makes saved jobs available to multiple users across a shared cluster. Starting the metastore is covered by the section on the sqoop-metastore tool.

The job tool allows you to create and work with saved jobs. Saved jobs remember the parameters used to specify a job, so they can be re-executed by invoking the job by its handle. If a saved job is configured to perform an incremental import, state regarding the most recently imported rows is updated in the saved job to allow the job to continually import only the newest rows. Although the Hadoop generic arguments must preceed any job arguments, the job arguments can be entered in any order with respect to one another.

Creating saved jobs is done with the --create action. This operation requires a -- followed by a tool name and its arguments.

The tool and its arguments will form the basis of the saved job. This creates a job named myjob which can be executed later. The job is not run. This job is now available in the list of saved jobs:. The exec action allows you to override arguments of the saved job by supplying them after a For example, if the database were changed to require a username, we could specify the username and password with:. If you have configured a hosted metastore with the sqoop-metastore tool, you can connect to it by specifying the --meta-connect argument.

This is a JDBC connect string just like the ones used to connect to databases for import. This parameter can also be modified to move the private metastore to a location on your filesystem other than your home directory. If you configure sqoop. The Sqoop metastore is not a secure resource. Multiple users can access its contents. For this reason, Sqoop does not store passwords in the metastore.

If you create a job that requires a password, you will be prompted for that password each time you execute the job. You can enable passwords in the metastore by setting sqoop.

Note that you have to set sqoop. Incremental imports are performed by comparing the values in a check column against a reference value for the most recent import. If an incremental import is run from the command line, the value which should be specified as --last-value in a subsequent incremental import will be printed to the screen for your reference. If an incremental import is run from a saved job, this value will be retained in the saved job.

Subsequent runs of sqoop job --exec someIncrementalJob will continue to import only newer rows than those previously imported. The metastore tool configures Sqoop to host a shared metadata repository. Clients must be configured to connect to the metastore in sqoop-site. Although the Hadoop generic arguments must preceed any metastore arguments, the metastore arguments can be entered in any order with respect to one another. Clients can connect to this metastore and create jobs which can be shared between users for execution.

This should point to a directory on the local filesystem. The port is controlled by the sqoop. Clients should connect to the metastore by specifying sqoop. This metastore may be hosted on a machine within the Hadoop cluster, or elsewhere on the network.

The merge tool allows you to combine two datasets where entries in one dataset should overwrite entries of an older dataset. For example, an incremental import run in last-modified mode will generate multiple datasets in HDFS where successively newer data appears in each dataset.

The merge tool will "flatten" two datasets into one, taking the newest available records for each primary key. Although the Hadoop generic arguments must preceed any merge arguments, the job arguments can be entered in any order with respect to one another. The merge tool runs a MapReduce job that takes two directories as input: a newer dataset, and an older one. These are specified with --new-data and --onto respectively. When merging the datasets, it is assumed that there is a unique primary key value in each record.

The column for the primary key is specified with --merge-key. Multiple rows in the same dataset should not have the same primary key, or else data loss may occur. To parse the dataset and extract the key column, the auto-generated class from a previous import must be used. You should specify the class name and jar file with --class-name and --jar-file. If this is not availab,e you can recreate the class using the codegen tool.

The merge tool is typically run after an incremental import with the date-last-modified mode sqoop import --incremental lastmodified …. Supposing two incremental imports were performed, where some older data is in an HDFS directory named older and newer data is in an HDFS directory named newer , these could be merged like so:. The following are some examples of database URLs:. This method specifies the user name and password required to access the DBMS with a Properties object.

Typically, in the database URL, you also specify the name of an existing database to which you want to connect. The samples in this tutorial use a URL that does not specify a specific database because the samples create a new database. This methods required an object of type java. Each JDBC driver contains one or more classes that implements the interface java.

The drivers for Java DB are org. EmbeddedDriver and org. See the documentation of your DBMS driver to obtain the name of the class that implements the interface java.

Any JDBC 4.