Friday, June 7, 2013

Hive Class 2

Hive Data Organized as

ØDatabases –Namespace that separates tables
ØTables -Homogeneous units of data with same schema
ØPartitions -A way of dividing a table into coarse-grained  Parts based on the value of a partition column, such as date.
ØBuckets -Tables or partitions may further be subdivided into buckets, to give extra structure to the data that may be used for more efficient queries.

Difference B/W  HIVE and SQL


HIVE class1

                                          What is Hive :
The Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage. Built on top of Apache Hadoop, it provides
ØTools to enable easy data extract/transform/load (ETL)
ØA mechanism to impose structure on a variety of data formats
ØAccess to files stored either directly in Apache HDFS or in other data storage systems such as Apache Hbase
ØQuery execution via Map Reduce
ØProvides SQL like query language –HIVE QL. Allows to plug in custom mappers/reducers in queries and also allows UDFs
ØHive is designed to enable easy data summarization, ad-hoc querying


         and analysis of large volumes of data


                              What Hive is Not ?
ØLatency for Hive queries is generally very high (minutes) even when data sets involved are very small (say a few hundred megabytes). As a result it cannot be compared with systems such as RDBMS.
ØHive is not designed for online transaction processing and does not offer real-time queries and row level updates. It is best used for batch jobs over large sets of immutable data (like web logs). Table update is achieved by transforming data into new table.
ØHive schema is not a ‘schema on write’. Does not verify the data when it is loaded. It is a ‘schema on read’ which verifies data on SQL query.
ØUnlike RDBMS table, More than one schema can be applied to same data
ØHive doesn’t define clear semantics for concurrent access to tables, which means applications need to build their own application-level concurrency or locking mechanism. The Hive team is actively working on improvements in all these areas.

                                       Hive Used For
ØLog Processing
ØText Mining
ØDocument Indexing
ØCustomer-facing business intelligence (Google analytics)
ØPredictive Modeling
ØHypothesis Testing


Hive Architecture
ØMetastore : Stores system catalog
ØDriver: manages life cycle of HiveQLquery as it moves thru’ HIVE; also manages session handle and session statistics
ØQuery compiler: Compiles HiveQLinto a directed acyclic graph of map/reduce tasks
ØExecution engines: The component executes the tasks in proper dependency order; interacts with Hadoop
ØHive Server : provides Thrift interface and JDBC/ODBC for integrating other applications.
ØClient components: CLI, web interface, jdbc/odbcinteface
ØExtensibility interface include SerDe, User Defined Functions and User Defined Aggregate Function.

Echo Systems Definitions

SQOOP: Used to import Data from RDBMS and also Export data into RDBMS.     
Flume:  To import Streaming Data
OOZIE:  To Schedule the Jobs(Hadoop) and Define Workflows


ZOOKEEPER:  State Maintenance (Controlling Data Locks)
 To store the records, failure of records nd Controlling the data locks.

Map Reduce: Map Reduce is a software framework that  allows developers to write programs that process  massive amounts of unstructured data in parallel across a distributed  cluster of processors or stand-alone computers.
The framework is divided into two parts:
Map Process:
In this process input is taken by the master
node,which divides it into smaller tasks and distribute them to the workers nodes. The workers nodes process these sub tasks and pass them back to the master node.
Reduce Process :
In this the master node combines all the answers provided by the worker nodes to get the results of the original task. The main advantage of Map reduce is that the map and
reduce are performed in distributed mode. Since each operation is independent, so each map can be
performed in parallel and hence reducing the net computing time.








Saturday, February 23, 2013

Hive+Xml Processing


                                  Hive+Xml Processing
First to understand XPATH() : by using this to parse XML data into String array.
Example: small xml data
   <rec><name>Babu</name><age>25</age><sex>male</sex></rec>
 <rec><name>Radha</name><age>23</age><sex>female</sex></rec>
NOTE:  xml data converted into hive table in two steps process
1.      Convert the xml data into array format  
2.      Array data can be converted into hive table format.
Process :
Step1: create the hive table
      Ex: Hive>create table hivexml(str string);
Step2: load the xmldata into hive table
     EX:    Hive>load data local inpath ‘xmlfile’ into table hivexml;
Ø  By this step load all local xml data into your hive table astise, so we can convert that data into STRING ARRAY format by using XPATH(), And then we can convert the array data into normal hive table data,
Step3:  convert the xml data into array format
     EX:   Hive>select xpath(str,’rec/*/text()’) from xmlhive;
Ø  OutPut:  ["Babu”,”25”,”male”]
                [“Radha”,”23”,”female”]

Explanation of ‘rec/*/text()’
                     rec: its define Node of xml same as XML DATA  (Check the xml data)
                       *: its define all the fields of xml data.
If you want specific fields simply mansion it  like below
Ex: Hive>select xpath(str,’rec/name/text()’)from xmlhive;

Ø  OutPut:  [“Babu”]
                [“Radha”]
Step4:  crate the HIVE table required columns
   EX: Hive> create  table newhivexml(name string,age  int,sex  string);
Ø  After creating the table to load the xml array format data into newhivexml table like below
Step5: 
     Hive> insert overwrite table newhivexml select xpath_string(str,'rec/name'),xpath_string(str,'rec/age'),xpath_string(str,'rec/sex')from hivexml;

Hive>select * from  newhivwxml ;
To get the data in table format like below.
name    age    sex
Babu    25      male
Radha   23      female

Thank you.
*  This note only for to get some basic idea purpose give me your feedback                                



Friday, February 8, 2013

Hadoop Ecosystem

 

The above diagram Clearly explain the Hadoop Echosystem, These are combination of different  Techknowledges all are doing different types of works shown the above program you can understand clearly     .
The Echosystem of Hadoop is

      Name            Purpose

    1. Hive          ( Data WareHouse)
    2. Pig            (Text Mining)
    3. Hbase       (Random Operations)
    4. Sqoop       (Export and Import)
    5. Flume        (Streaming Data)
    6. Ooziee       (Scheduler nd Workflow Design)
    7.Zookeeper (State Maintenance)


1. Hive :
  •   Hive is  a Data Warehouse in Hadoop Environment.
  •  To process Structured and Semi-structured and Un-Structured data.
  •  Un-Structured data can be processed by converting into Structured data.


2. Pig :
  • Pig is used for Text analytic (mining)
  • Pig is Processed for Xml and Json data.
  • Even though data is Structured and impossible of Hive (hql) can be processed by Pig.
  • The additional Functionalists (not possible of Pig) can be done by Using UDF (User Define Functions)
  • The Pig UDF's can be done in following languages
                       i.e : Java,Ruby,Python,Java script, C++, etc........  
  •   Hive also supports UDF's hive udf's can be done in
                         i.e:  Java,Ruby,Python,C++,R Program etc........ 
  • When you run high query's in pig automatically java Map Reduce code will be build by the frame it will be submitted by JVM.







Tuesday, February 5, 2013

Hadoop Training

Hi Welcome to Programming Hadoop