You can use less memory, and start working on the result sooner, if you instruct the library not to buffer the result. The downside is that the locks and other resources on the server will remain open while your application is interacting with the library. The code seems to indicate that you fetch rows only when you need them, in the while loop.
The while loop simply iterates through the buffer. Programming languages have different ways to override buffering. You can also specify this when connecting, which will make every statement unbuffered:.
Each MySQL connection, or thread , has a state that shows what it is doing at any given time. As a query progresses through its lifecycle, its state changes many times, and there are dozens of states. The MySQL manual is the authoritative source of information for all the states, but we list a few here and explain what they mean:. The thread is waiting for a new query from the client. The thread is either executing the query or sending the result back to the client.
The thread is waiting for a table lock to be granted at the server level. The thread is checking storage engine statistics and optimizing the query. This can mean several things: the thread might be sending data between stages of the query, generating the result set, or returning the result set to the client. On very busy servers, you might see an unusual or normally brief state, such as statistics , begin to take a significant amount of time. This usually indicates that something is wrong.
Before even parsing a query, MySQL checks for it in the query cache, if the cache is enabled. This operation is a case sensitive hash lookup.
If MySQL does find a match in the query cache, it must check privileges before returning the cached query. This is possible without parsing the query, because MySQL stores table information with the cached query.
If the privileges are OK, MySQL retrieves the stored result from the query cache and sends it to the client, bypassing every other stage in query execution. The query is never parsed, optimized, or executed. You can learn more about the query cache in Chapter 5. The next step in the query lifecycle turns a SQL query into an execution plan for the query execution engine.
It has several sub-steps: parsing, preprocessing, and optimization. Errors for example, syntax errors can be raised at any point in the process. Our goal is simply to help you understand how MySQL executes queries so that you can write better ones. Next, the preprocessor checks privileges. This is normally very fast unless your server has large numbers of privileges. See Chapter 12 for more on privileges and security.
The parse tree is now valid and ready for the optimizer to turn it into a query execution plan. A query can often be executed many different ways and produce the same result. MySQL uses a cost-based optimizer, which means it tries to predict the cost of various execution plans and choose the least expensive.
The unit of cost is a single random four-kilobyte data page read. This result means that the optimizer estimated it would need to do about 1, random data page reads to execute the query. It bases the estimate on statistics: the number of pages per table or index, the cardinality number of distinct values of indexes, the length of rows and keys, and key distribution.
The statistics could be wrong. The server relies on storage engines to provide statistics, and they can range from exactly correct to wildly inaccurate. There are two basic types of optimizations, which we call static and dynamic. Static optimizations can be performed simply by inspecting the parse tree. For example, the optimizer can transform the WHERE clause into an equivalent form by applying algebraic rules.
They can be performed once and will always be valid, even when the query is reexecuted with different values. In contrast, dynamic optimizations are based on context and can depend on many factors, such as which value is in a WHERE clause or how many rows are in an index. They must be reevaluated each time the query is executed. The difference is important in executing prepared statements or stored procedures.
MySQL can do static optimizations once, but it must reevaluate dynamic optimizations every time it executes a query. MySQL sometimes even reoptimizes the query as it executes it. Here are some types of optimizations MySQL knows how to do:. MySQL can recognize this and rewrite the join, which makes it eligible for reordering. MySQL applies algebraic transformations to simplify and canonicalize expressions.
It can also fold and reduce constants, eliminating impossible constraints and constant conditions. These rules are very useful for writing conditional queries, which we discuss later in the chapter. Indexes and column nullability can often help MySQL optimize away these expressions. It can even do this in the query optimization stage, and treat the value as a constant for the rest of the query.
Similarly, to find the maximum value in a B-Tree index, the server reads the last row. This literally means the optimizer has removed the table from the query plan and replaced it with a constant. When MySQL detects that an expression can be reduced to a constant, it will do so during optimization. Arithmetic expressions are another example. Perhaps surprisingly, even something you might consider to be a query can be reduced to a constant during the optimization phase.
One example is a MIN on an index. This can even be extended to a constant lookup on a primary key or unique index. It will then treat the value as a constant in the rest of the query. MySQL executes this query in two steps, which correspond to the two rows in the output. The first step is to find the desired row in the film table. It can do this because the optimizer knows that by the time the query reaches the second step, it will know all the values from the first step.
MySQL can sometimes use an index to avoid reading row data, when the index contains all the columns the query needs. We discussed covering indexes at length in Chapter 3. MySQL can convert some types of subqueries into more efficient alternative forms, reducing them to index lookups instead of separate queries.
MySQL can stop processing a query or a step in a query as soon as it fulfills the query or step. For instance, if MySQL detects an impossible condition, it can abort the entire query. You can see this in the following example:. This query stopped during the optimization step, but MySQL can also terminate execution sooner in some cases.
For example, the following query finds all movies without any actors: [ 42 ]. This query works by eliminating any films that have actors. Each film might have many actors, but as soon as it finds one actor, it stops processing the current film and moves to the next one because it knows the WHERE clause prohibits outputting that film.
For instance, in the following query:. In many database servers, IN is just a synonym for multiple OR clauses, because the two are logically equivalent. Not so in MySQL, which sorts the values in the IN list and uses a fast binary search to see whether a value is in the list.
This is O log n in the size of the list, whereas an equivalent series of OR clauses is O n in the size of the list i. You may end up just defeating it, or making your queries more complicated and harder to maintain for zero benefit. In general, you should let the optimizer do its work. Some of the options are to add a hint to the query, rewrite the query, redesign your schema, or add indexes. The engines may provide the optimizer with statistics such as the number of pages per table or index, the cardinality of tables and indexes, the length of rows and keys, and key distribution information.
The optimizer can use this information to help it decide on the best execution plan. In sum, it considers every query a join—not just every query that matches rows from two tables, but every query, period including subqueries, and even a SELECT against a single table. Each of the individual queries is a join, in MySQL terminology—and so is the act of reading from the resulting temporary table.
This means MySQL runs a loop to find a row from a table, then runs a nested loop to find a matching row in the next table. It continues until it has found a matching row in each table in the join. It tries to build the next row by looking for more matching rows in the last table.
It keeps backtracking until it finds another row in some table, at which point, it looks for a matching row in the next table, and so on. This query execution plan applies as easily to a single-table query as it does to a many-table query, which is why even a single-table query can be considered a join—the single-table join is the basic operation from which more complex joins are composed.
Read it from left to right and top to bottom. Figure Swim-lane diagram illustrating retrieving rows using a join.
MySQL executes every kind of query in essentially the same way. In short, MySQL coerces every kind of query into this execution plan. Still other queries can be executed with nested loops, but perform very badly as a result. We look at some of those later. Instead, the query execution plan is actually a tree of instructions that the query execution engine follows to produce the query results.
The final plan contains enough information to reconstruct the original query. Any multitable query can conceptually be represented as a tree. For example, it might be possible to execute a four-table join as shown in Figure This is what computer scientists call a balanced tree. This is not how MySQL executes the query, though.
As we described in the previous section, MySQL always begins with one table and finds matching rows in the next table. The most important part of the MySQL query optimizer is the join optimizer , which decides the best order of execution for multitable queries. It is often possible to join the tables in several different orders and get the same results. The join optimizer estimates the cost for various plans and tries to choose the least expensive one that gives the same result. You can probably think of a few different query plans.
This should be efficient, right? This is quite a different plan from the one suggested in the previous paragraph. Is this really more efficient? This shows why MySQL wants to reverse the join order: doing so will enable it to examine fewer rows in the first table. The difference is how many of these indexed lookups it will have to do:. If the server scans the actor table first, it will have to do only index lookups into later tables.
In other words, the reversed join order will require less backtracking and rereading. The reordered query had an estimated cost of , while the estimated cost of forcing the join order was 1, Reordering joins is usually a very effective optimization. In most cases, the join optimizer will outperform a human. The join optimizer tries to produce a query execution plan tree with the lowest achievable cost. When possible, it examines all potential combinations of subtrees, beginning with all one-table plans.
Unfortunately, a join over n tables will have n -factorial combinations of join orders to examine. This is called the search space of all possible query plans, and it grows very quickly—a table join can be executed up to 3,, different ways!
When the search space grows too large, it can take far too long to optimize the query, so the server stops doing a full analysis. MySQL has many heuristics, accumulated through years of research and experimentation, that it uses to speed up the optimization stage. This is because the results for one table depend on data retrieved from another table. These dependencies help the join optimizer reduce the search space by eliminating choices. In other words, the join conditions are based on primary key column in one table and foreign key column in another table.
If the join conditions are not based on primary key column and foreign key column, they must be based on columns with common data values of the same or similar data types. When joining two tables on a composite primary key, all columns that make up the composite primary key must be used in the join. If not, duplicate records will be returned in the result set.
In the tutorials about joins, only standard SQLs are used. They will not be covered in these tutorials. The purpose is to make your SQL code reusable to other Database systems without too many modifications.
Products table in Northwind database only stores SupplierID which is a foreign key pointing back to SupplierID column in suppliers table. If we want to know the supplier's name for a product, we need to write a query to join with suppliers table to get this information. In this practice, a single result set is returned which displays product name and the supplier's name for each product.
Most of the queries in the tutorials need Northwind MySQL database, you can download the database script on this page. Query result set - 77 rows returned:. For example, consider. Although this query's restrictions are superficially similar to the previous example, the semantics are different because a row must be emitted for each row of A that has no matching row in the join of B and C.
Therefore the planner has no choice of join order here: it must join B to C and then join A to that result.
Accordingly, this query takes less time to plan than the previous query. For example, these three queries are logically equivalent:. But if we tell the planner to honor the JOIN order, the second and third take less time to plan than the first. This effect is not worth worrying about for only three tables, but it can be a lifesaver with many tables. Other possible values are discussed below. In this example, the number of possible join orders is reduced by a factor of 5.
Constraining the planner's search in this way is a useful technique both for reducing planning time and for directing the planner to a good query plan. Cross joins. Join tables based on an inequality of field values. Delete a join. A database is a collection of tables of data that bear logical relationships to each other. You use relationships to connect tables by fields that they have in common.
A table can be part of any number of relationships, but each relationship always has exactly two tables. In a query, a relationship is represented by a join. When you add tables to a query, Access creates joins that are based on relationships that have been defined between the tables. You can manually create joins in queries, even if they do not represent relationships that have already been defined. If you use other queries instead of or in addition to tables as sources of data for a query, you can create joins between the source queries, and also between those queries and any tables that you use as sources of data.
Joins behave similarly to query criteria in that they establish rules that the data must match to be included in the query operations. Unlike criteria, joins also specify that each pair of rows that satisfy the join conditions will be combined in the recordset to form a single row.
There are four basic types of joins: inner joins, outer joins, cross joins, and unequal joins. This article explores each type of join you can use, why you use each type, and how to create the joins. Joins are to queries what relationships are to tables: an indication of how data in two sources can be combined based on data values they have in common. This line between the tables represents the join. Double-click a join to open the Join Properties dialog box depicted and review or change the join.
Joins are sometimes directional. This area of the dialog box shows you which table is which in the join, and which fields are used to join the tables. This area determines the type of join: option 1 is an inner join, 2 is a left outer join, and 3 is a right outer join. Fields from both tables can be used, and data that pertains to a given task is displayed from each.
In an inner join, no other data is included. In an outer join, unrelated records from one table are also included in the query results. Cross joins and unequal joins are advanced join types and are rarely used, but you should know about them to have a full understanding of how joins work. An inner join is one in which Access only includes data from a table if there is corresponding data in the related table, and vice versa. Most of the time, you will use inner joins.
An outer join is like an inner join, but adds the remaining rows from one of the tables. Outer joins are directional: a left outer join includes all the records from the left table — the first table in the join — and a right outer join includes all the records from the right table — the second table in the join.
In some systems, an outer join can include all rows from both tables, with rows combined when they correspond. However, you can use a cross join and criteria to achieve the same effect. Most of the time, a cross join is a side effect of adding two tables to a query and then forgetting to join them. Access interprets this to mean that you want to see every record from one table combined with every record from the other table — every possible combination of records.
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