By Edward Capriolo, Dean Wampler
Need to maneuver a relational database program to Hadoop? This entire advisor introduces you to Apache Hive, Hadoop’s information warehouse infrastructure. You’ll quick how you can use Hive’s SQL dialect—HiveQL—to summarize, question, and study huge datasets saved in Hadoop’s dispensed filesystem.
This example-driven advisor indicates you ways to establish and configure Hive on your surroundings, offers an in depth evaluate of Hadoop and MapReduce, and demonstrates how Hive works in the Hadoop environment. You’ll additionally locate real-world case stories that describe how businesses have used Hive to unravel specified difficulties related to petabytes of data.
- Use Hive to create, adjust, and drop databases, tables, perspectives, capabilities, and indexes
- Customize information codecs and garage concepts, from records to exterior databases
- Load and extract info from tables—and use queries, grouping, filtering, becoming a member of, and different traditional question methods
- Gain top practices for growing person outlined features (UDFs)
- Learn Hive styles you can use and anti-patterns you might want to avoid
- Integrate Hive with different information processing programs
- Use garage handlers for NoSQL databases and different datastores
- Learn the professionals and cons of operating Hive on Amazon’s Elastic MapReduce
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Extra info for Programming Hive
HiveSequenceFileOutputFormat, Hive makes use of the InputFormat whilst analyzing facts from the desk, and it makes use of the OutputFormat while writing info to the desk. notice InputFormat reads key-value pairs from documents; Hive presently ignores the most important and works in simple terms with the information present in the price by way of default. reason why the foremost, which comes from TextInputFormat, is an extended integer that represents the byte offset within the block (which isn't really person data). the remainder of the bankruptcy describes different facets of the desk metadata. dossier codecs We mentioned in textual content dossier Encoding of knowledge Values that the easiest info structure to take advantage of is the textual content structure, with no matter what delimiters you wish. it's also the default layout, reminiscent of making a desk with the clause saved AS TEXTFILE. The textual content dossier structure is handy for sharing info with different instruments, corresponding to Pig, Unix textual content instruments like grep, sed, and awk, and so on. It’s additionally handy for viewing or enhancing documents manually. besides the fact that, the textual content layout isn't really house effective in comparison to binary codecs. we will use compression, as we mentioned in bankruptcy eleven, yet we will be able to additionally achieve extra effective utilization of disk area and higher disk I/O functionality through the use of binary dossier codecs. SequenceFile the 1st substitute is the SequenceFile structure, which we will specify utilizing the saved AS SEQUENCEFILE clause in the course of desk production. series documents are flat documents which includes binary key-value pairs. while Hive converts queries to MapReduce jobs, it makes a decision at the applicable key-value pairs to take advantage of for a given list. The series dossier is a regular layout supported by means of Hadoop itself, so it's a suitable selection while sharing documents among Hive and different Hadoop-related instruments. It’s much less compatible to be used with instruments outdoor the Hadoop atmosphere. As we mentioned in bankruptcy eleven, series documents will be compressed on the block and list point, that is very priceless for optimizing disk house usage and I/O, whereas nonetheless aiding the power to separate records on block obstacles for parallel processing. one other effective binary structure that's supported natively by way of Hive is RCFile. RCFile so much Hadoop and Hive garage is row orientated, that's effective generally. The potency may be attributed to numerous components: such a lot tables have a smaller quantity (1−20) of columns. Compression on blocks of a dossier is effective for facing repeating information, and lots of processing and debugging instruments (more, head, awk) paintings good with row-oriented info. now not all instruments and information shops take a row-oriented procedure; column-oriented association is an effective garage choice for specific sorts of information and purposes. for instance, if a given desk has countless numbers of columns yet such a lot queries use just a couple of of the columns, it's wasteful to test complete rows then discard lots of the facts. although, if the knowledge is kept through column rather than by way of row, then merely the information for the specified columns should be learn, enhancing functionality. It additionally seems that compression on columns is usually very effective, specially whilst the column has low cardinality (only a number of certain entries).