Machine search software vendor Splunk has released Splunk Hadoop Connect, which facilitates the passing of data between Splunk and Hadoop. It will eventually replace the company's M5 distribution.Ī number of other companies have also released new products in conjunction with the Hadoop conference. The beta version of M7 is now available to select users. MapR's M7 version of HBase is fully binary compatible with Apache HBase, and can run Apache HBase within the cluster with M7's own version of the data store software.
And in this version of HBase, users can create more than a trillion tables. The row and cell sizes have been increased to accommodate larger objects as well, up to 1GB in size. The database now supports in-memory columns. The company claims that inserts and updates occur much more quickly. The new database does not compact, or compress data, which should allow the database to perform more consistently. The database comes with a number of other new features as well. You can read directly from the snapshots," Norris said. HBase reads directly from those tables, so now you have instant recovery. "The files and tables are side-by-side in the volumes and directories. MapR uses its own file system, which has been extended to handle tables as well. As a result, it can take up to 30 minutes to switch to a back-up copy of HBase. While a generic version of HBase does offering mirroring capabilities, it relies on HDFS (Hadoop File System), which is a "write-once" file system, said Jack Norris, vice president of marketing for MapR Technologies. The database can now be replicated and mirrored, so that if one copy goes down the system can switch to the backup copy. MapR is adding new features to make its own HBase database distribution more reliable. A number of business intelligence software providers have already tested their own products against Impala, including Karmasphere, MicroStrategy, Pentaho, and Tableau.
The company has released the Impala source code under an Apache Foundation license. As a result, it runs queries much faster than Hive.Įventually, Impala will be the basis of a Cloudera commercial offering, called the Cloudera Enterprise RTQ (Real-Time Query), though the company has not specified a release date. The Impala database engine uses the Hive metadata directory, though it bypasses MapReduce, while still offering SQL as an interface, said Charles Zedlewski, Cloudera vice president of products. This process can be especially tedious when multiple sub-queries need to be made to form a single query. This approach, however, can be slow, since Hive uses the Map Reduce framework, which requires the results of each query be written to disk. Until now, organizations tended to use Hive to execute SQL querying against HBase. As a result, Hadoop vendors are rushing to meet the new demands required by these new workloads.Ĭloudera is working on a database engine, code-named Impala, that can query datasets stored on the HBase database through SQL (Structured Query Language). "We find that most of the Hadoop workloads are things you couldn't previously do, like combining disparate data types," said Kirk Dunn, Cloudera chief operating officer.