Pig Latin. Dominique Fonteyn Wim Leers. Universiteit Hasselt
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1 Pig Latin Dominique Fonteyn Wim Leers Universiteit Hasselt
2 Pig Latin is an English word game in which we place the rst letter of a word at the end and add the sux -ay. Pig Latin becomes igpay atinlay banana becomes anana-bay What does this have to do with computer sciences?
3 Will the real Pig Latin please stand up? Pig Latin is a language developed by Yahoo! designed for ad-hoc data analysis. Combination of high-level declarative querying (SQL style) low-level procedural programming (map-reduce)
4 First example Find the average pagerank of high-pagerank URLs for each suciently large category in a table urls (url, category, pagerank). SQL: SELECT category, AVG(pagerank) FROM urls WHERE pagerank > 0.2 GROUP BY category HAVING COUNT(*) > 10 6
5 First example (2) Find the average pagerank of high-pagerank URLs for each suciently large category in a table urls (url, category, pagerank). PIG LATIN: good_urls = FILTER urls BY pagerank > 0.2; groups = GROUP good_urls BY category; big_groups = FILTER groups BY COUNT(good_urls) >10 6 ; output = FOREACH big_groups GENERATE category, AVG(good_urls.pagerank);
6 First example (3) Pig Latin programs are sequences of steps Each step carries out a single data transformation Transformations are fairly high-level e.g. ltering, grouping, aggregation low-level manipulations are unnecessary Writing Pig Latin programs is similar to specifying a query execution plan and thus easier for programmers to understand and control how their data is being processed.
7 Presentation Overview 1 Features and Motivation 2 Pig Latin, the Language 3 Implementation 4 Practical Notes 5 Copresentation
8 Presentation Overview 1 Features and Motivation 2 Pig Latin, the Language 3 Implementation 4 Practical Notes 5 Copresentation
9 Dataow Language Pig Latin is a high-level data ow language. The user species a sequence of steps. Each step performs only a single, high-level data transfomation. It is not necessary that the operations be executed in the order of that sequence. Usage of high-level relational algebra-style primitives like group and filter allows traditional database optimizations.
10 Dataow Language (2) Find the URLs of all pages that are classied as spam, but have a high pagerank. spam_urls = FILTER urls BY isspam(url); culprit_urls = FILTER spam_urls BY pagerank > 0.8; isspam() is a user-dened function and may be expensive not the most ecient method
11 Dataow Language (3) More ecient would be, Find the URLs of all pages that are classied as spam, but have a high pagerank. culprit_urls = FILTER urls BY pagerank > 0.8; spam_urls = FILTER spam_urls BY isspam(url); 1 get all high pagerank pages rst 2 invoke isspam() only on these high pagerank pages This optimization can be done automatically by the system.
12 Quick Start and Interoperability Pig Latin is designed to support ad-hoc data analysis. queries can be run directly over data les the user must provide a function to parse the content into tuples Similar for output.
13 Quick Start and Interoperability (2) Stored schemas are strictly optional. Schema information can be provided on the y, or even not at all. Because...
14 Quick Start and Interoperability (2) Stored schemas are strictly optional. Schema information can be provided on the y, or even not at all. Because... PIGS EAT ANYTHING!
15 Nested Data Model Programmers often think in terms of nested data structures. Example: Capture information of each pig in a collection of pig farms. Map<pigFarmId, Set<pig>>
16 Nested Data Model (2) Databases allow only at tables, i.e., columns are atomic elds. pig_farms: (pigfarmid, pigfarmname,...) pigs: (pigid, pigname,...) pig_info: (pigfarmid, pigid)
17 Nested Data Model (3) Pig Latin oers a exible, fully nested data model and allows complex, non-atomic data types as eld or table. Some reasons for having a nested data model: closer to how programmers think and thus much more natural to them than normalization allows programmers to easily write a rich set of user-dened functions
18 UDFs as First-Class Citizens Custom processing is a signicant part of analysing data. Pig Latin has extensive support for user-dened functions (UDFs). All aspects of Pig Latin processing can be customized through the use of UDFs. Input and output of UDFS in Pig Latin follow the nested data model. A UDF can take non-atomic parameters as input, and also output non-atomic values.
19 UDFs as First-Class Citizens (2) Example: Find the top 10 URLs according to pagerank for each category. groups = GROUP urls BY category; output = FOREACH groups GENERATE category, top10(urls); Here, top10() is a UDF that accepts a set of URLs, and outputs a set containing the top 10 URLs by pagerank for that group. The nal output contains non-atomic elds: there is a tuple for each category, and one of the elds is the set of top 10 URLs.
20 UDFs as First-Class Citizens (3) Practical notes UDFs are written in Java. Yahoo! is building support for other languages, including C/C++, Perl (Erlpay) and Python (Ythonpay).
21 Parallellism Required Processing web-scale data requires parallelism. Pig Latin includes a small set of carefully chosen primitives that can be easily parallelized. Other primitives that do not lend themselves to ecient parallel evaluation have been deliberately excluded. They can still be carried out by UDFs. The user is then responsible for how ecient his programs are and whether they will be parallelized.
22 Debugging Environment Getting a data processing program right usually takes many iterations. With web-scale data, a single iteration can take many minutes or hours. The usual run-debug-run cycle can be very slow and inecient. Pig comes with a novel interactive debugging environment that generates concise example data tables illustrating the output of each step of the user's program.
23 Debugging Environment (2)
24 Presentation Overview 1 Features and Motivation 2 Pig Latin, the Language 3 Implementation 4 Practical Notes 5 Copresentation
25 Data Model Pig uses a rich, yet simple data model consisting of 4 types: Atom Tuple Bag Map
26 Data Model (3)
27 Specifying Input Data The rst step is to specify what the input data les are, and how the le contents are to be deserialized. We use the LOAD command. We assume the input le is a bag, i.e., it contains a sequence of tuples.
28 Specifying Input Data (2) queries = LOAD 'query_log.txt' USING myload() AS (userid, querystring, timestamp); input le is query_log.txt input is converted into tuples by using a custom myload deserializer loaded tuples have 3 elds named userid, querystring and timestamp
29 Specifying Input Data (3) queries = LOAD 'query_log.txt' USING myload() AS (userid, querystring, timestamp); Both the USING and AS clause are optional. If no deserializer is specied, Pig uses a default one that expects a plain text, tab-delimited le. If no schema is used, elds must be referred to by position instead of by name. For readability it is desirable to include schemas.
30 Per-tuple Processing The FOREACH command applies some processing to each tuple of a data set. expanded_queries = FOREACH queries GENERATE userid, expandquery(querystring); Each tuple of the bag queries should be processed independently to produce an output tuple. The rst eld is the userid eld of the input tuple. The second eld is the result of applying the UDF expandquery() to the querystring eld of the input tuple.
31 Per-tuple Processing (2) The GENERATE clause can be followed by a list of expressions. A common expression type is attening. The FLATTEN keyword eliminates nesting by extracting the elds of the tuples in the bag, and making them elds of the tuple being output by GENERATE. This removes one level of nesting. expanded_queries = FOREACH queries GENERATE userid, FLATTEN(expandQuery(queryString));
32 Per-tuple Processing (3)
33 Per-tuple Processing (4)
34 Discarding Unwanted Data The FILTER command discards all data that is not of interest. Example: Get rid of bot trac. real_queries = FILTER queries BY userid neq 'bot'; comparison operators: ==,!=, <, >,... (numbers) eq, neq (strings) logical operators: AND, OR, NOT
35 Discarding Unwanted Data (2) We can use UDFs as well. Example: Get rid of bot trac. real_queries = FILTER queries BY NOT isbot(userid);
36 Getting Related Data Together It is often necessary to group together related tuples from one or more data sets. This is done with the COGROUP command. Example: we have 2 data sets specied results: (querystring, url, position) revenue: (querystring, adslot, amount)
37 Getting Related Data Together (2) Example: group together all search result data and revenue data for the same query string grouped_data = COGROUP results BY querystring, revenue BY querystring; Output: grouped_data: (group, results, revenue) rst eld is the group identier, the value of querystring each next eld is a bag, one for each input being cogrouped and is named the same as the alias of that input
38 Getting Related Data Together (3) Example: join all search result data and revenue data for the same query string join_result = JOIN results BY querystring, revenue BY querystring; What is the dierence with COGROUP?
39 Getting Related Data Together (4)
40 Getting Related Data Together (5) When there is only one data set, we use GROUP. grouped_revenue = GROUP results BY querystring;
41 Getting Related Data Together - Summarized When there is one data set GROUP When there are two or more data sets JOIN COGROUP JOIN equals a COGROUP followed by FLATTEN
42 Map-Reduce in Pig Latin The GROUP and FOREACH statements allow us to express a map-reduce program. map_result = FOREACH input GENERATE FLATTEN(map(*)); key_groups = GROUP map_result BY $0; output = FOREACH key_groups GENERATE reduce(*);
43 Other Commands Other commands are, UNION CROSS ORDER DISTINCT
44 Nested Operations Each command operates over one or more bags or tuples as input. When we have nested bags within tuples, we can nest some commands within a FOREACH command. grouped_revenue = group revenue BY querystring; query_revenue = FOREACH grouped_revenue { top_slot = FILTER revenue BY adslot eq 'top'; GENERATE querystring, SUM(top_slot.amount), SUM(revenue.amount); };
45 Asking for Output Write results to le with STORE STORE query_revenues INTO 'myoutput' USING mystore();
46 Presentation Overview 1 Features and Motivation 2 Pig Latin, the Language 3 Implementation 4 Practical Notes 5 Copresentation
47 Implementation Pig Latin is implemented by the Pig sytem. Programs are compiled into map-reduce jobs and executed by Hadoop. It is an open source project in the Apache incubator.
48 Building a Logical Plan The Pig interpreter rst parses the Pig Latin commands and veries that the referred input les and bags are valid. e.g. when entering c = COGROUP a BY..., b BY..., Pig veries that a and b are already dened It builds a logical plan for each dened bag.
49 Building a Logical Plan (2) When dening a new bag, the logical plan is constructed by combining the logical plans for the input bags, and the current command. e.g. when entering c = COGROUP a BY..., b BY..., The logical plan for c consists of a cogroup command with the plans for a and b as input.
50 Building a Logical Plan (3) When the logical plans are constructed, no processing is carried out. Processing is only triggered when invoking a STORE command. Then the logical plan is compiled into a physical plan and executed. This lazy style of execution permits in-memory pipelining and other optimizations.
51 Map-Reduce Plan Compilation Map-reduce provides the ability to do a large-scale group by the map tasks assign keys for grouping the reduce tasks process a group at a time
52 Presentation Overview 1 Features and Motivation 2 Pig Latin, the Language 3 Implementation 4 Practical Notes 5 Copresentation
53 Practical Notes More information can be found at pig.apache.org. Pig is a project under active development. New features are to be added: safe optimizer user interfaces external functions unied environment
54 Presentation Overview 1 Features and Motivation 2 Pig Latin, the Language 3 Implementation 4 Practical Notes 5 Copresentation
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