spark sql join optimization

SQL statement optimization: optimization of JOIN, LEFT JOIN and RIGHT JOIN statements; sqljoin In database applications, we often need to perform multi-table queries on the database. However, when the data volume is large, multi-t

query optimizations (Optimizer) are optimized after the logical plan, and finally mapped into a physical plan, converted into Rdd execution.For more information on Sparksql parsing and execution, please refer to the article "Parsing and execution of SQL". For syntax parsing, parsing, and query optimization, this article does not elaborate, this article focuses on the physical implementation of

Products A table of changes in the price of goods, orders, records each purchase of goods and datesMatch orders and products based on a non-equivalent join in Spark SQL, counting the prices of the items in each orderSlow-changing commodity price listWangzai milk, there was a price change.scala> val products = sc.parallelize(Array( | ("旺仔牛奶", "2017-01-01", "

SQL statement Optimization-query different rows of the two tables NOT IN, NOT EXISTS, Join query Left Join, existsleft In actual development, we often need to compare the differences between two or more tables and compare the data that is the same and the data is different. At this time, we can use the following three

WHERE s.id=1Catalyst presses the original query through the predicate, id=1 the selection operation first, filtering the majority of the data, and using the property merge to make the final projection only once to the final reserved Class attribute column.(4) Join optimizationSpark SQL deeply draws on the essence of traditional database query optimization techno

Tags: LVS and List serve log enter war field dataWhen you use join for two dataframe in Spark SQL, the value of the field as a connection contains a null value . Because the meaning of the null representation is unknown, neither does it know that the comparison of null values in SQL with any other value (even if null)

outer joinrows =Sqlctx.sql ("Select Table1.name, Table1.title, table2.fraction from table1 to outer join table2 on table1.name = Table2.name"). Collect () printrows (rows)#Right outer joinrows =Sqlctx.sql ("Select Table1.name, Table1.title, table2.fraction from table1 right outer join table2 on table1.name = Table2.name"). Collect ()Print "============================================="printrows (rows)#Full

when the user writes data to other tables in Beeline The workaround is to close the permissions setting for HDFs, and in Hadoop 2.7.3, close the HDFs permission check for the parameter Hdfs-site.xml Property > name >dfs.permissions.enabledname> value >falsevalue> Property > 3 Spark SQL executes SQL-like commands directly in Beelin

Tags: SHUF implementation. So data operator class yarn spark SQL Boost performanceSelection of storage formats:Do you take row or column-type storage? The number of times a column store is written, and the loss time is much faster when queriedselection of compression formats:Consider the compression speed and the compressed file of the partition compression can be less storage space, improve data transfer s

(TableName) can remove a table from the cache. With sqlcontext.setconf (), set the Spark.sql.inMemoryColumnarStorage.batchSize parameter (default 10000) to configure the units of the Columnstore. 6. Broadcast Join table: Spark.sql.autoBroadcastJoinThreshold, Default 10485760 (Ten MB). In the case of sufficient memory, you can increase its size, the parameters set a table at the time of the join, maximum in

. Method Three: Using connection query Connection queries include: 1. Self-connect (join equals inner JOIN): Query result is data that exists on both sides 2. Left join to the left join: return all data on the right, return to the back, null not present 3. Right join: Retu

==> cache data in memory---> Performance tuning is primarily about putting data into memory operations---> Usage examples:// reading data from Oracle database, generating NBSP;DATAFRAMEVALNBSP;ORACLEDF =spark.read.format ("jdbc") .option ("url", " Jdbc:oracle:thin:@192.168.10.100:1521/orcl.example.com ") . Option ("DBTable", "Scott.emp") .option ("User", "Scott ") .option (" Password "," Tiger ") .load // will DataFrame register as a table oracledf.registertemptable ("EMP")// execute the query,

Performance optimization ParametersThe tuning parameters for the spark SQL performance are as follows:code exampleimportjava.util.list;importorg.apache.spark.sparkconf;import Org.apache.spark.api.java.javasparkcontext;importorg.apache.spark.sql.api.java.javasqlcontext;import org.apache.spark.sql.api.java.row;importorg.apache.spark.sql.hive.api.java.javahivecontex

SQL Optimization-count, table join sequence, conditional order, in, exist, countexist 1. About count I have read some articles about count (*) and count (Column). Is the efficiency of count (column) higher than that of count? In fact, I personally think that count (*) and count (column) are not comparable at all. count (*) counts the total number of rows in the t

reads 0 Times, lob pre-read 0 Times.Table ' Worktable ' . Scan count 0 , Logical read 0 Physical reads 0 Times, pre-read 0 Times, lob logic reads 0 Physical lob reads 0 Times, lob pre-read 0 Times.Table ' Ufv3a7n71178865841875 ' . Scan count 1 , Logical read 576 Physical reads 0 Times, pre-read 0 Times, lob logic reads 0 Physical lob reads 0 Times, lob pre-read 0 Times.Table ' Workflowbase ' . Scan count 1

Tags: blog col tps character encoding share picture stuck left join GES imageSQL is stuck on execution, then ... Kill the process.Look at the size of the table.The first reaction is to add the index, and then explain looked at what index, the result is very awkward, three tables, only walked an index ... A group of people in that tangle for the hair can not go index.Inadvertently found that the character encoding of a table is GBK. The other two are U

, logical read 18,984 times, physical read 0 times, read 0 times, LOB logic read 0 times, lob physical read 0 times, lob read 0 times.Table ' Workflowbase '. Scan count 3, logical read 1589 times, physical read 0 times, read 0 times, LOB logic read 0 times, lob physical read 0 times, lob read 0 times.Two IO gap:56952+1589=58,541 times (using in) 18984+1589=20573 times (using exists) Using exists is 2.8 times times the in and query performance is greatly improved. EXISTS makes queries faster be

characteristics, depending on what you like. Sql One way to use Spark SQL is to execute SQL queries through SQL statements. When SQL is used in a programming language, its return result is encapsulated as a dataframe. DataFrame D

Column Based on the SELECT column to return the final result. Second,Two-table join query: the product (Cartesian Product) of the two tables is filtered using the ON condition and connection type to form an intermediate table. Then, the records of the intermediate table are filtered Based ON the WHERE condition, return the query result based on the column specified by SELECT.Third,Multi-table join query: q

condition to form an intermediate table (this intermediate table is invisible to users ); then SELECT the corresponding column based on the SELECT column to return the final result.Second,Two-table join query: the product (Cartesian product) of the two tables is filtered using the ON condition and connection type to form an intermediate table. then, the records of the intermediate table are filtered based ON the WHERE condition, return the query resu