hive query optimization

Label:In the original: Remember the SQL query optimization of a bitter forceRecently in the maintenance of the company project, the need to load a page, a total load of more than 4,000 data, it takes 35 seconds, if the data grew to 40,000, I estimate a few minutes can not be confused. Lying trough, to me is the user's words estimate can not stand, while idle of nothing, want to optimize it, walk you. First

Label: SQL Server Query optimization method The reasons for the slow query are many, the following are common 1, no index or no index (this is the most common problem of slow query, is the defect of program design)2, I/O throughput is small, forming a bottleneck effect3. No computed column created causes

name to run on any member server. The system operates as if each member server has a copy of the original table, but there is only one member table and one distributed partitioned view on each server. The location of the data is transparent to the application.11. Rebuild the index DBCC REINDEX, DBCC INDEXDEFRAG, shrink data and log DBCC SHRINKDB,DBCC shrinkfile. Sets the auto-shrink log. For large databases do not set the database autogrow, it will degrade the performance of the server. There's

ORDER (40 + 2 = 42 pages). The output table contains about 95 rows on each page, with a total of 160 pages. Writing and accessing these pages triggers 5*160 = 800 reads and writes, and the index reads and writes 892 pages. 3. Connect the output and the part to get the final result: SELECT pvvn_by_pn. *, part. part_desc FROM pvvn_by_pn, part WHERE pvvn_by_pn.part_num = part. part_num Drop table pvvn_by_pn In this way, the query reads pvvn_by_pn sequen

can greatly optimize the query speed, basically can be completed within dozens of milliseconds. The restriction is only used to explicitly know the ID, but when the table is established, the Basic ID field is added, which brings a lot of traversal to the paging query.There is another way to do this:selectfromwhereid1000001limit100;Of course, you can also use in the way to query, this method is often used i

queries are the most commonly used operations in database technology. The query operation process is relatively simple, first from the client issued a query SQL statement, the database server after receiving the SQL statement sent by the client, execute the SQL statement, and then return the results of the query to the client. Although the process is very simple,

text search needs 10+s, is the need for 1s. MongoDB client configuration, you can propose to make spring injection, set the maximum number of connections and so on. Mongoclientoptions options = mongoclientoptions.builder (). Maxwaittime (1000 * 2) . Connectionsperhost (500 ). Build (); Mongoclient = new Mongoclient (arrays.aslist new ServerAddress ("10.205.68.57", 8700), new ServerAddress (" 10.205.68.15 ", 8700), new ServerAddress (" 10.205.69.13 ", 8700)), options); Mongoclient.setre

Label:MySQL Database optimization pt-query-digest use I. Introduction of PT-QUERY-DIGEST Tools Pt-query-digest is a tool for analyzing MySQL slow query, which can analyze Binlog, general log, Slowlog, or through show Processlist or the MySQL protocol data captured by Tcpdump

SQL Server query performance optimization analysis transactions and locks (I) SQL Server query performance optimization analysis transactions and locks (ii) SQL Server query performance optimization analysis transactions and locks

Essential SQL query optimization techniques to speed up Website access and SQL access In this article, I will introduce how to identify queries that cause performance problems, how to locate their problems, and how to quickly fix these problems and other methods to speed up the query. You must know that a website with quick access can be liked by users, help web

processing overhead for insert, delete, and update operations. In addition, too many composite indexes, in the case of single-field index, generally have no value; Conversely, it also reduces performance when data is being deleted, especially for tables that are frequently updated, with greater negative impact.---------------------| optimization of SQL statements |---------------------By using indexes correctly, you can use resources to make the data

meaning of the expression "specific optimizations" in the table above Q Contrast S8 and S9, can be seen, toprowdb to S9 did not provide optimization, and PostgreSQL, MySQL can optimize, this toprowdb need to work hard Q contrast S11, toprowdb and MySQL are stronger than PostgreSQL In the first part of the subquery optimization, we found that MySQL's in subquery opti