SQL query optimization tools determine the best way to execute a query by analyzing different query plans and seeing which one delivers the best performance. After obtaining the results, the query optimization tools use the most efficient query plan to run the query.
Azure Query Performance Insight provides query analyses for single and pooled databases. The tool also helps determine which queries consume the most resources in users' workloads. The results allow users to identify which queries need optimization.
Sql Query Optimizer Tool Mysql Insert
The Analyzer tool allows users to monitor performance, client machines, users, and applications via a dashboard, represents their performance and any anomalies, and identifies which SQL query to focus on.
The Paessler PRTG Network Monitor tool monitors Microsoft SQL, MySQL, Oracle SQL, and PostgreSQL databases. PRTG Network Monitor makes SQL query monitoring and optimization simple and measures the time needed for executing SQL query requests.
AppOptics APM is a cloud-based performance monitoring tool that features database optimization utilities. It identifies the root cause of query performance issues and helps users resolve them.
To toggle the aurora_parallel_query parameter at the session level, use the standard methods to change a client configuration setting. For example, you can do so through the mysql command line or within a JDBC or ODBC application. The command on the standard MySQL client is set session aurora_parallel_query = 'ON'/'OFF'. You can also add the session-level parameter to the JDBC configuration or within your application code to turn on or turn off parallel query dynamically.
To toggle the aurora_pq parameter at the session level, for example through the mysql command line or within a JDBC or ODBC application, use the standard methods to change a client configuration setting. For example, the command on the standard MySQL client is set session aurora_pq = 'ON'/'OFF'. You can also add the session-level parameter to the JDBC configuration or within your application code to turn on or turn off parallel query dynamically.
You can also turn on or turn off parallel query at the session level, for example through the mysql command line or within a JDBC or ODBC application. To do so, use the standard methods to change a client configuration setting. For example, the command on the standard MySQL client is set session aurora_parallel_query = 'ON'/'OFF' for Aurora MySQL 1.23 or 2.09 and higher. Before Aurora MySQL 1.23, the command is set session aurora_pq = 'ON'/'OFF'.
You can use the aurora_pq_force session variable to override the parallel query optimizer and request parallel query for every query. We recommend that you do this only for testing purposes The following example shows how to use aurora_pq_force in a session.
In typical operation, you don't need to perform any special actions to take advantage of parallel query. After a query meets the essential requirements for parallel query, the query optimizer automatically decides whether to use parallel query for each specific query.
In addition to the Amazon CloudWatch metrics described in Viewing metrics in the Amazon RDS console, Aurora provides other global status variables. You can use these global status variables to help monitor parallel query execution. They can give you insights into why the optimizer might use or not use parallel query in a given situation. To access these variables, you can use the SHOW GLOBAL STATUS command. You can also find these variables listed following.
The number of times parallel query wasn't chosen because a high percentage of the table data (currently, greater than 95 percent) was already in the buffer pool. In these cases, the optimizer determines that reading the data from the buffer pool is more efficient. An EXPLAIN statement can increment this counter even though the query isn't actually performed.
In cases where parallel query isn't chosen, you can typically deduce the reason from the other columns of the EXPLAIN output. For example, the rows value might be too small, or the possible_keys column might indicate that the query can use an index lookup instead of a data-intensive scan. The following example shows a query where the optimizer can estimate that the query will scan only a small number of rows. It does so based on the characteristics of the primary key. In this case, parallel query isn't required.
The output showing whether parallel query will be used takes into account all available factors at the moment that the EXPLAIN statement is run. The optimizer might make a different choice when the query is actually run, if the situation changed in the meantime. For example, EXPLAIN might report that a statement will use parallel query. But when the query is actually run later, it might not use parallel query based on the conditions then. Such conditions can include several other parallel queries running concurrently. They can also include rows being deleted from the table, a new index being created, too much time passing within an open transaction, and so on.
If the optimizer estimates that the number of returned rows for a query block is small, parallel query isn't used for that query block. The following example shows a case where a greater-than operator on the primary key column applies to millions of rows, which causes parallel query to be used. The converse less-than test is estimated to apply to only a few rows and doesn't use parallel query.
The same considerations apply for not-equals tests and for range comparisons such as less than, greater than or equal to, or BETWEEN. The optimizer estimates the number of rows to scan, and determines whether parallel query is worthwhile based on the overall volume of I/O.
The optimizer rewrites any query using a view as a longer query using the underlying tables. Thus, parallel query works the same whether table references are views or real tables. All the same considerations about whether to use parallel query for a query, and which parts are pushed down, apply to the final rewritten query.
Typically, after an INSERT statement, the data for the newly inserted rows is in the buffer pool. Therefore, a table might not be eligible for parallel query immediately after inserting a large number of rows. Later, after the data is evicted from the buffer pool during normal operation, queries against the table might begin using parallel query again.
The statistics gathered by the ANALYZE TABLE statement help the optimizer to decide when to use parallel query or index lookups, based on the characteristics of the data for each column. Keep statistics current by running ANALYZE TABLE after DML operations that make substantial changes to the data within a table.
Aurora includes built-in caching mechanisms, namely the buffer pool and the query cache. The Aurora optimizer chooses between these caching mechanisms and parallel query depending on which one is most effective for a particular query.
This function binds the parameters to the SQL query and tells the database what the parameters are. The "sss" argument lists the types of data that the parameters are. The s character tells mysql that the parameter is a string.
With dbForge MySQL performance tuning tool, you can: Optimize queries with the EXPLAIN plan
Monitor session statistics
Compare query profiling results
Identify the most expensive queries
Great performance related post. I actually reached your post when looking into a very similar performance issue with WooCommerce. I ended up taking the recommendations from this online sql optimizer I found on google. The query now runs in under 1 second. The indexes I added are: ALTER TABLE oiz6q8a_postmeta ADD INDEX oiz6q8a_postmeta_index_1 (meta_value, post_id); ALTER TABLE oiz6q8a_posts ADD INDEX oiz6q8a_posts_index_1 (post_type, ID); ALTER TABLE oiz6q8a_woocommerce_software_licences ADD INDEX oiz6q8a_woocommerce_software_licences_index_1 (key_id, order_id, software_product_id); ALTER TABLE oiz6q8a_woocommerce_software_subscriptions ADD INDEX oiz6q8a_woocommerce_software_subscriptions_index_1 (key_id, next_payment_date);
So far, you configured ProxySQL to use your MySQL server as a backend and connected to the backend using ProxySQL. Now, you are ready to use mysqlslap to benchmark the query performance without caching.
In this step, you will download a test database so you can execute queries against it with mysqlslap to test the latency without caching, setting a benchmark for the speed of your queries. You will also explore how ProxySQL keeps records of queries in the stats_mysql_query_digest table.
mysqlslap is a load emulation client that is used as a load testing tool for MySQL. It can test a MySQL server with auto-generated queries or with some custom queries executed on a database. It comes installed with the MySQL client package, so you do not need to install it; instead, you will download a database for testing purposes only, on which you can use mysqlslap.
In this command you are adding a new record to the mysql_query_rules table; this table holds all the rules applied before executing a query. In this example, you are adding a value for the cache_ttl column that will cause the matched query by the given digest to be cached for a number of milliseconds specified in this column. You put 1 in the apply column to make sure that the rule is applied to queries.
Have you ever seen a WHERE 1=1 condition in a SELECT query. I have, within many different queries and across many SQL engines. The condition obviously means WHERE TRUE, so it's just returning the same query result as it would without the WHERE clause. Also, since the query optimizer would almost certainly remove it, there's no impact on query execution time. So, what is the purpose of the WHERE 1=1? That is the question that we're going to answer here today!
As stated in the introduction, we would expect the query optimizer to remove the hard-coded WHERE 1=1 clause, so we should not see a reduced query execution time. To confirm this assumption, let's run a SELECT query in Navicat both with and without the WHERE 1=1 clause. 2ff7e9595c
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