spark sql join optimization

); then select the corresponding Column Based on the Select column to return the final result.2. join queries for two tables: product (Cartesian Product) of the two tables and filter using the on condition and connection type to form an intermediate table. Then, filter records of the intermediate table based on the where condition, return the query result based on the column specified by select.3. Multi-Table connection query: perform queries on the

right table read, in sortmergejoin such implementation is very natural.There is no process to partition by join key in Broadcasthashjoin, so this optimization is missing. In some cases of adaptive execution, however, we can retrieve this optimization by using accurate statistics between the stages: if Sortmergejoin is converted to Broadcasthashjoin at runtime, a

The oom problem in Spark is the following two scenarios Memory overflow in map execution Memory overflow after shuffle The memory overflow in map execution represents the operation of all map types, including: Flatmap,filter,mappatitions, and so on. The shuffle operation for memory overflow after shuffle includes operations such as Join,reducebykey,repartition. After summarizing my underst

Tags: create NTA rap message without displaying cat stream font1. What is Spark streaming?A, what is Spark streaming?Spark streaming is similar to Apache Storm, and is used for streaming data processing. According to its official documentation, Spark streaming features high throughput and fault tolerance.

Spark Asia-Pacific Research Institute wins big Data era public forum fifth: Spark SQL Architecture and case in-depth combat, video address: http://pan.baidu.com/share/link?shareid=3629554384uk= 4013289088fid=977951266414309Liaoliang Teacher (e-mail: [email protected] qq:1740415547)President and chief expert, Spark Asia

. Textfile("Spark/sql/people.txt") Import org. Apache. Spark. SQL. _ Val Rowrdd = People_rdd. Map(x=x. Split(",")). Map(x= = Row (x(0),x(1). Trim. ToInt)) Import org. Apache. Spark. SQL. Types. _ Val Schema = Structtype (Array (St

tree or make a sequential adjustment.Take a familiar join operation as an example, and give a sample of join optimizations. A join B is equivalent to B join A, but the sequential adjustment can have a significant effect on the performance of the execution, that is, the contrast chart before and after the adjustment.As

; Structfield (column,stringtype,true)) Val schemarating = Structtype ("Userid::movieid". Split ("::"). Map (column => Structfield (column,stringtype,true)) Val Rowuser:rdd[row] = Userrdd.map (line => Row (line._1,line._2)) val Rowrating:rdd[row] = Ratingrdd.map (line = Gt Row (line._1,line._2)) Val Userdatafaram = Spark.createdataframe (rowuser,schemauser) val Ratingdatafram = spark.cr Eatedataframe (rowrating,schemarating) ratingdatafram.filter (S "MovieID = 3578").

Tags: android http io using AR java strong data spSpark SQL Architecture and case drill-down video address:http://pan.baidu.com/share/link?shareid=3629554384uk=4013289088fid=977951266414309Liaoliang Teacher (e- mail:[email protected] QQ: 1740415547)President and chief expert, Spark Asia-Pacific Research Institute, China's only mobile internet and cloud computing big data synthesizer.In

formula or algorithm calculation (cumulative, tired) 2, For some of the more complex operations that are similar to string concatenation of each key, you can measure it yourself, In fact, you can use Reducebykey to do that sometimes. But not too good to achieve. is definitely helpful for performance if you can do that. (Shuffle basically takes up more than 90% of the performance consumption of the entire spark job, can be tuned for shuffle, which

1. Self-connectSuppose the following file exists:[root@bluejoe0 ~]# cat categories.csv 1,生活用品,02,数码用品,13,手机,24,华为Mate7,3The format of each row is: Category ID, category name, parent class IDNow to output the name of the parent category for each category, similar to the SQL self-join, notice that the foreign key of the join is actually the parent class ID.First ge

other words, if you want to ensure high execution efficiency, users need to do a lot of SQL optimization, the use of experience greatly reduced. To make the best possible effort to ensure that users are familiar with SQL optimization, the quality of the submitted SQL,

let MySQL to judge it.EXPLAINSELECT * fromT1INNER JOINT2 onT1.type=T2.type; EXPLAINSELECT * fromT2INNER JOINT1 onT1.type=T2.type; EXPLAINSELECT * fromT1JOINT2 onT1.type=T2.type; EXPLAINSELECT * fromT2JOINT1 onT1.type=T2.type; EXPLAINSELECT * fromT1,t2WHERET1.type=T2.type; EXPLAINSELECT * fromT2,t1WHERET1.type=T2.type; +----+-------+------+------+--------+----------------------------------------------------+ |Id| Table |Type| Key |Rows|Extra| +----+-------+------+------+--------+---

slow like a snail, it's O (N) with an O (1) hash table. However, changing this query isIDcolumn, notNameYou will see a completely different story. In this case, it nests a loop of two queries, butINNER JOINThe version can replace the index scan with one of the seeking-which means that this is simply an order of magnitude faster with a large number of rows. So more or less on what several paragraphs above, it is almost certain that the index or index covers the problem with one or more very smal

is a classic problem, the more "back" of the query data slower (depending on the index type on the table, for the B-tree structure of the index, the same in SQL Server)SELECT * FROM T order by ID limit m,n.That is, with the increase of M, querying the same number of data, it will be more and more slowIn the face of this problem, there is a classic approach, similar to (or variant) the following notationis to first find out the ID in the paging range,

, only to do this step now.So essentially Ds/sql has become the addition to the RDD API, another set of common, unified interactive APIs that cover streaming, batch processing, interactive querying, machine learning and other big data fields. This is the first time we have achieved such a unification, and now it is only on the spark platform to achieve, it is the use of big data and learning threshold furth

execution of the physical operator is implemented by the system itself.Catalyst StatusWhat is provided in the parser is a simple Scala-written SQL parser, with limited semantic support and should be standard SQL.In terms of rules, the provision of optimization rules is relatively basic (and pig/hive is not so rich), but some of the optimization rules are actuall

. When clustered index seek is executed, the actual data record is finally scanned. In this process, tableb. col2 =? This condition also avoids an additional filter operation. This is why the filter operation is not performed in MySQL 1.4. F) construct the returned result set. Same as step d in step 2. 1.6 nested loop usage conditions If any Join Operation meets the nested loop usage conditions, the SQL