Most databases provide the capability to store large amounts of data in a single field. Depending on whether this data is textual or binary in nature, it is usually represented as a CLOB or BLOB column in the table. These “large objects” are often handled specially by the database itself. In particular, most tables are physically laid out on disk as in Figure . When scanning through rows to determine which rows match the criteriafor a particular query, this typically involves reading all columns of each row from disk. If large objects were stored “inline” in this fashion, they would adversely affect the performance of such scans. Therefore, large objects are often stored externally from their rows, as in Figure . Accessing a large object often requires “opening” it through the reference contained in the row.
The difficulty of working with large objects in a database suggests that a system such as Hadoop, which is much better suited to storing and processing large, complex data objects, is an ideal repository for such information. Sqoop can extract large objects from tables and store them in HDFS for further processing.
As in a database, MapReduce typically materializes every record before passing it along to the mapper. If individual records are truly large, this can be very inefficient.
As shown earlier, records imported by Sqoop are laid out on disk in a fashion very similar to a database’s internal structure: an array of records with all fields of a record concatenated together. When running a MapReduce program over imported records, each map task must fully materialize all fields of each record in its input split. If the contents of a large object field are only relevant for a small subset of the total numberof records used as input to a MapReduce program, it would be inefficient to fully materialize all these records. Furthermore, depending on the size of the large object, full materialization in memory may be impossible.
To overcome these difficulties, Sqoop will store imported large objects in a separate file called a LobFile. The LobFile format can store individual records of very large size (a 64-bit address space is used). Each record in a LobFile holds a single large object. The LobFile format allows clients to hold a reference to a record without accessing the record contents. When records are accessed, this is done through a java.io.InputStream (for binary objects) or java.io.Reader (for character-based objects).
When a record is imported, the “normal” fields will be materialized together in a text file, along with a reference to the LobFile where a CLOB or BLOB column is stored. For example, suppose our widgets table contained a BLOB field named schematic holding the actual schematic diagram for each widget.
An imported record might then look like:
Figure . Large objects are usually held in a separate area of storage; the main row storage contains indirect references to the large objects
The externalLob(...) text is a reference to an externally stored large object, stored in LobFile format (lf) in a file named lobfile0, with the specified byte offset and length inside that file.
When working with this record, the Widget.get_schematic() method would return an object of type BlobRef referencing the schematic column, but not actually containing its contents. The BlobRef.getDataStream() method actually opens the LobFile and returns an InputStream allowing you to access the schematic field’s contents.
When running a MapReduce job processing many Widget records, you might need to access the schematic field of only a handful of records. This system allows you to incur the I/O costs of accessing only the required large object entries, as individual schematics may be several megabytes or more of data.
The BlobRef and ClobRef classes cache references to underlying LobFiles within a map task. If you do access the schematic field of several sequentially ordered records, they will take advantage of the existing file pointer’s alignment on the next record body.
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