Programming
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Log file analysis is popular usecase in Big Data world. Log files contains evidence of historical events that an application witnessed under their execution environment. Monitoring applications intend to find out traces of actual events that happened during program execution. Several analysis usecases are possible from simply counting occurrence of some event to specific processing.
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. EMP and DEPT tables are pretty popular between Oracle users. These tables were very handy in quickly trying new queries. Also, there exists a DUAL table in Oracle that was pretty useful in evaluate expressions, like- “Select (SYSDATE + 1/24) as OneHourFromNow FROM DUAL“. These tables doesn’t exists in Hive, but we can create
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. There are few type of UDFs that we can write in Hive. Functions that act on each column value passed to it, e.g. Select Length(name) From Customer Specific functions written for a specific data type (simple UDFs) Generic functions written to working with more than one data type Functions that act on a group
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. HCatalog is an extension of Hive and in a nutshell, it exposes the schema information in Hive Metastore such that applications outside of Hive can use it. The objective of HCatalog is to hold the following type of information about the data in HDFS – Location of the data Metadata about the data (e.g.
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. There are a few type of UDFs that we can write in Hive. Functions that act on each column value passed to it, e.g. Select Length(name) From Customer Specific functions written for a specific data type Generic functions written to working with more than one data type (GenericUDF) Functions that act on a group
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. emp = LOAD ‘/path/to/data/file/on/hdfc/Employees.txt’ [ USING PigStorage(‘ ‘) ] AS ( emp_id: INT, name: CHARARRAY, joining_date: DATETIME, department: INT, salary: FLOAT, mgr_id: INT, residence: BAG { b:(addr1: CHARARRAY, addr2: CHARARRAY, city: CHARARRAY) }) ; The Alias for data in file “Employees.txt” is emp and using emp,
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. UDFs (User Defined Functions) are ways in pig to extend its functionality. There are two type of UDFs that we can write in pig – Evaluate (extends from EvalFunc base class) Load/Store functions (extends from LoadFunc base class) Here we will stepwise develop an Evaluate UDF. Lets start by conceptualizing a UDF (named VowelCount)
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. — emp = LOAD ‘Employees.txt’ … Data in text file resembles the “EMP” table in Oracle — dept = LOAD ‘Dept.txt’ …….. Data in text file resembles the “DEPT” table in Oracle — Filter data in emp to only those whose job is Clerk. Filtered_Emp = FILTER emp BY (job == ‘CLERK’); — Supports
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. Simple: INT and FLOAT are 32 bit signed numeric datatypes backed by java.lang.Integer and java.lang.Float Simple: LONG and DOUBLE are 64 bit signed numeric Java datatypes Simple: CHARARRAY (Unicode backed by java.lang.String) Simple: BYTEARRAY (Bytes / Blob, backed by Pig’s DataByteArray class that wraps byte[]) Simple: BOOLEAN (“true” or “false” case sensitive) Simple: DATETIME
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. Pig Statements — Load command loads the data — Every placeholder like “A_Rel” and “Filter_A” are called Alias, and they are useful — in holding the relation returned by pig statements. Aliases are relations (not variables). A_Rel = LOAD ‘/hdfs/path/to/file’ [AS (col_1[: type], col_2[: type], col_3[: type], …)] ; — Record set returned by
Vipul Pathak 