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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]
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Greenplum Database basics in the age of Hadoop (1 of 2)

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The Greenplum Database constructs on the basis of open origin database PostgreSQL. It firstly purposes like a information storage and uses a shared-nothing architecture|shared-nothing, astronomically collateral (computing)|massively collateral handling (MPP) design.

How Greenplum works...
In this design, information is partitioned athwart numerous section servers, and every one section controls and commands a clearly different part of the altogether data; there is no disk-level parting nor information argument amid sections.
Greenplum Database’s collateral request optimizer changes every one request into a material implementation design.
Greenplum’s optimizer utilizes a cost-based set of rules to appraise prospective implementation designs, bears a worldwide view of implementation athwart the computer array, and circumstances in the charges of moving information amid knots.

The ensuing request designs hold customary relational database transactions like well like collateral motion transactions that report as and how information ought to be moved amid knots throughout request implementation. Commodity Gigabit Ethernet and 10-gigabit Ethernet technics is applied aimed at the transference amid knots.

The design part of Greenplum...
During implementation of every one node within the design, numerous relational transactions are treated by Pipeline (computing)|pipelining: the capacity to start a assignment beforehand its forerunner assignment has finished, to rise effectual alikeness. For instance, when a table audit is seizing place, lines picked may be pipelined in to a connect procedure. 30+High+Paying+IT+Jobs
  • Internally, the Greenplum configuration uses record delivering and segment-level replication and delivers converted to be operated by largely automatic equipment a procedure by which a system automatically transfers control to a duplicate system when it detects a fault or failure. At the storage layer, RAID methods may disguise flat circular plate disappointments.
  • At the configuration layer, Greenplum copies section and principal information to different knots to establish that the mislaying of a engine must not influence the altogether database obtainability.

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