Low-level MySQL question for you: we are storing UUIDs as bin(16) in the DB and are using them as a primary key. We are in the process of changing the UUID stored to time-based. We want to optimize clustered index insertions to be append-only, but that will only work if we are sure of how MySQL adds the values to the b-tree. Does anyone know if MySQL b-tree for bin types uses the least or most significant bit first and then goes right-to-left or left-to-right respectively?
Binary string types are like other string types, except they have no character set. That is, the bytes are treated as literal byte values, with no encoding. Otherwise, they sort just like strings: left to right.
Try an experiment to test this:
create table mytable (id int primary key, b binary(16), key(b));
insert into mytable values
(1, unhex('BBBBBBBBBBBBBBBB7777777777777777')),
(2, unhex('7777777777777777BBBBBBBBBBBBBBBB'))
mysql> select id, hex(b) from mytable order by b;
+----+----------------------------------+
| id | hex(b) |
+----+----------------------------------+
| 2 | 7777777777777777BBBBBBBBBBBBBBBB |
| 1 | BBBBBBBBBBBBBBBB7777777777777777 |
+----+----------------------------------+
If the string were sorted by the least significant bit, the order would be opposite.
I used EXPLAIN to prove that this query uses the index on column b:
explain select id, hex(b) from mytable order by b;
+----+-------------+---------+------------+-------+---------------+------+---------+------+------+----------+-------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+---------+------------+-------+---------------+------+---------+------+------+----------+-------------+
| 1 | SIMPLE | mytable | NULL | index | NULL | b | 17 | NULL | 2 | 100.00 | Using index |
+----+-------------+---------+------------+-------+---------------+------+---------+------+------+----------+-------------+
Yes, it is possible to make Type 1 UUIDs into BINARY(16) for 'trivial` indexing in virtually chronological order.
All ports of MySQL have identical ordering of the bits and bytes on disk. So this discussion is OS-independent.
Pre-8.0, see my UUID blog: UUIDs
8.0: See the uuid functions; they do essentially the same as mentioned in my blog
MariaDB 10.7 has a UUID datatype; no need for adding function calls. But they rearrange the bits differently.
Related
Need to search through table foo
foo structure is
id | something
There is an INDEX for field something
I want to search BETWEEN AS INT:
SELECT CAST(something as INT) as something_int FROM foo foo_1
WHERE something_int > 1 AND something_int < 9999
In this case the INDEX would be used or be broken?
WHERE some_varchar BETWEEN '1' AND '2000' -- fast but probably incorrect
WHERE some_varchar BETWEEN 1 AND 2000 -- slow but correct
WHERE some_int BETWEEN '1' AND '2000' -- fast
WHERE some_int BETWEEN 1 AND 2000 -- fast (same as previous)
What is happening?
When comparing text to numeric, the text side is converted to numeric, and then numeric comparisons are performed.
Text to text comparison does a string comparison; numeric to numeric does a numeric comparison.
Above, I say "slow" meaning that no index can be used; "fast" if an index can be used.
The "incorrect" one has the same problem as sorting a set of numbers in a VARCHAR and then wondering why the list is out of order: 1,10,11,...,19,2,20,...,29,3, ...
CAST() is just an explicit version of the implicit conversion I am talking about here.
CAST('2000' TO INT) is done "at compile time", so the Optimizer sees it as simply 2000 (numeric, no function call).
some_varchar >= 1 on the other hand, is turned into CAST(some_varchar TO INT) >= 1 in the second example above.
As a Rule of Thumb, "hiding a column in a function call prevents using an index. See "sargable" in Wikipedia.
No, the index will not be used.
CREATE TABLE foo(something varchar(20) primary key) engine=myisam;
INSERT INTO foo VALUES ('1|abc'), ('3456|def');
DESCRIBE SELECT * FROM foo WHERE CAST(something as INT) BETWEEN 1 AND 2000;
+------+-------------+-------+-------+---------------+---------+---------+------+------+--------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+------+-------------+-------+-------+---------------+---------+---------+------+------+--------------------------+
| 1 | SIMPLE | tt | index | NULL | PRIMARY | 82 | NULL | 2 | Using where; Using index |
+------+-------------+-------+-------+---------------+---------+---------+------+------+--------------------------+
DESCRIBE SELECT * FROM foo WHERE something BETWEEN '1' AND '2000';
+------+-------------+-------+-------+---------------+---------+---------+------+------+--------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+------+-------------+-------+-------+---------------+---------+---------+------+------+--------------------------+
| 1 | SIMPLE | tt | range | PRIMARY | PRIMARY | 82 | NULL | 1 | Using where; Using index |
+------+-------------+-------+-------+---------------+---------+---------+------+------+--------------------------+
Notice how possible_keys is NULL for the first query (and rows found is 2).
Note: this can happen even if the character set of the query doesn't match that of the index.
Create a separate INT index (e.g. using the function index syntax).
I have a table with ~6M rows that is extracting around ~20,000-30,000 rows per query with index optimization. However, as a lot of people are extracting these rows consecutively (every 30 seconds or so) the site will often time out for people.
I recently migrated the database to a 3-server MySQL cluster with a huge amount of RAM (512GB per server) and the performance haven't improved a lot.
I was wondering if partioning would be the best way to proceed to improve performance. As I have absolutely no experience with partioning I thought I would ask here.
My question is, all of these rows have a column that will either have the value 0, 1, 2 or 3.
Would it be possible somehow to place all the rows with value 1 in a certain column on one partition, and all rows with value 2 in a column in another one? And would they move automatically based on the value being updated in the primary table? And most importantly, could it help out with performance as it would only have to look through finding 1 row in 20,000-30,000 instead of 6,000,000
Yes, MySQL supports partitioning. You can define the partitions pretty well, like:
CREATE TABLE MyTable (
id INT AUTO_INCREMENT PRIMARY KEY,
somestuff INT,
otherstuff VARCHAR(100),
KEY (somestuff)
) PARTITION BY HASH(id) PARTITIONS 4;
INSERT INTO MyTable () VALUES (), (), (), ();
You can verify how many rows are in each partition after this:
SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME='MyTable';
+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p0 | 1 |
| p1 | 1 |
| p2 | 1 |
| p3 | 1 |
+----------------+------------+
However, there are two things that trip people up when they try to use partitioning in MySQL:
First, as https://dev.mysql.com/doc/refman/5.7/en/partitioning-limitations-partitioning-keys-unique-keys.html says:
every unique key on the table must use every column in the table's partitioning expression.
This means if you want to partition by somestuff in the example above, you can't. That would fail the requirement that primary key include the column named in the partition expression.
ALTER TABLE MyTable PARTITION BY HASH(somestuff) PARTITIONS 4;
ERROR 1503 (HY000): A PRIMARY KEY must include all columns in the table's partitioning function
You can get around this by removing any primary key or unique key constraints from your table, but that leaves you with kind of a malformed table.
Second, partitioning speeds up queries only if you can take advantage of partition pruning, and this happens only if your query conditions include the column used in the partition expression.
mysql> EXPLAIN PARTITIONS SELECT * FROM MyTable WHERE SomeStuff = 3;
+----+-------------+---------+-------------+------+---------------+-----------+---------+-------+------+-------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+---------+-------------+------+---------------+-----------+---------+-------+------+-------+
| 1 | SIMPLE | MyTable | p0,p1,p2,p3 | ref | somestuff | somestuff | 5 | const | 4 | NULL |
+----+-------------+---------+-------------+------+---------------+-----------+---------+-------+------+-------+
Note this says it will need to scan partitions p0,p1,p2,p3 — i.e. the whole table. There is no partition pruning, therefore no performance improvement because it is not reducing the number of rows examined.
If you do search for a specific value in the column used in the partitioning expression, you can see that MySQL is able to reduce the number of partitions it scans:
mysql> EXPLAIN PARTITIONS SELECT * FROM MyTable WHERE id = 3;
+----+-------------+---------+------------+-------+---------------+---------+---------+-------+------+-------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+---------+------------+-------+---------------+---------+---------+-------+------+-------+
| 1 | SIMPLE | MyTable | p3 | const | PRIMARY | PRIMARY | 4 | const | 1 | NULL |
+----+-------------+---------+------------+-------+---------------+---------+---------+-------+------+-------+
Partitioning can help a lot in very specific circumstances, but partitioning isn't as versatile as most people think.
In most cases, it's better to define more specific indexes in your table to support the queries you need to run.
A "select distinct col1,col2 from table1" where col1 and col2 are of type TEXT and table1 has about 65K rows works fine with MySQL 5.5.58. Now that I've upgraded to MySQL 5.7.20 it takes almost an hour! Does anyone know of any changes to MySQL that may be causing this? Does anyone have any suggestions how col1 and col2 should be optimally indexed for this query, or what other settings I should check to make this query run faster? I don't get the feeling that indexes are even being used since EXPLAIN says it's using a temporary table and no keys:
mysql> `
explain SELECT DISTINCT author,sort_author from itemsbyauthor;
+----+-------------+---------------+------------+------+---------------+------+---------+------+-------+----------+-----------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+---------------+------------+------+---------------+------+---------+------+-------+----------+-----------------+
| 1 | SIMPLE | itemsbyauthor | NULL | ALL | NULL | NULL | NULL | NULL | 64727 | 100.00 | Using temporary |
+----+-------------+---------------+------------+------+---------------+------+---------+------+-------+----------+-----------------+
1 row in set, 1 warning (0.00 sec)
In many cases, MySQL doesn't use prefix indexes properly and it seems this is one of these cases.
Do you really need the column type to be TEXT?
From the column names, it looks like the columns are holding author names, which seems like a relatively short string (let's say, up to 50 or 100 characters)?
I would re-consider the column type and try to alter it to VARCHAR with a fixed size, instead of TEXT.
Then, add a compound index that includes both columns.
I was expecting this query to use a key.
mysql> DESCRIBE TABLE Foo;
+-------+-------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+-------+-------------+------+-----+---------+----------------+
| id | bigint(20) | NO | PRI | NULL | auto_increment |
| name | varchar(50) | NO | UNI | NULL | |
+-------+-------------+------+-----+---------+----------------+
mysql> EXPLAIN SELECT id FROM Foo WHERE name='foo';
+----+-------------+-------+------+---------------+------+---------+------+------+-----------------------------------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+------+---------------+------+---------+------+------+-----------------------------------------------------+
| 1 | SIMPLE | NULL | NULL | NULL | NULL | NULL | NULL | NULL | Impossible WHERE noticed after reading const tables |
+----+-------------+-------+------+---------------+------+---------+------+------+-----------------------------------------------------+
Foo has a unique index on name, so why isn't the index being used in the SELECT?
From the MySQL Manual page entitled EXPLAIN Output Format:
Impossible WHERE noticed after reading const tables (JSON property:
message)
MySQL has read all const (and system) tables and notice that the WHERE
clause is always false.
and the definition of const tables, from the Page entitled Constants and Constant Tables:
A MySQL constant is something more than a mere literal in the query.
It can also be the contents of a constant table, which is defined as
follows:
A table with zero rows, or with only one row
A table expression that is restricted with a WHERE condition,
containing expressions of the form column = constant, for all the
columns of the table's primary key, or for all the columns of any of
the table's unique keys (provided that the unique columns are also
defined as NOT NULL).
The second reference is a page and half long. Please refer to it.
const
const
The table has at most one matching row, which is read at the start of
the query. Because there is only one row, values from the column in
this row can be regarded as constants by the rest of the optimizer.
const tables are very fast because they are read only once.
const is used when you compare all parts of a PRIMARY KEY or UNIQUE
index to constant values. In the following queries, tbl_name can be
used as a const table:
SELECT * FROM tbl_name WHERE primary_key=1;
SELECT * FROM tbl_name WHERE primary_key_part1=1 AND
primary_key_part2=2;
It could be because that the said table Foo very less volume of data. In such case optimizer will choose to do table scan rather than looking through index.
As MySQL Documentation clearly says
Indexes are less important for queries on small tables, or big tables
where report queries process most or all of the rows. When a query
needs to access most of the rows, reading sequentially is faster than
working through an index. Sequential reads minimize disk seeks, even
if not all the rows are needed for the query.
This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
Disadvantages of quoting integers in a Mysql query?
I have a very simple table Called Device on MYSql database.
+-----------------------------------+--------------+------+-----+----------------+
| Field | Type | Null | Key | Extra |
+-----------------------------------+--------------+------+-----+----------------+
| DTYPE | varchar(31) | NO | | |
| id | bigint(20) | NO | PRI | auto_increment |
| dateCreated | datetime | NO | | |
| dateModified | datetime | NO | | |
| phoneNumber | varchar(255) | YES | MUL | |
| version | bigint(20) | NO | | |
| oldPhoneNumber | varchar(255) | YES | | |
+-----------------------------------+--------------+------+-----+----------------+
This table has more than 100K records. I am running a very simple query
select * from AttDevice where phoneNumber = 5107357058;
This query takes almost 4-6 second, But when I change this query a little bit as shown below.
select * from AttDevice where phoneNumber = '5107357058';
It takes almost no time to get executed.
Notice that phoneNumber column is varchar. I don't understand why the former case takes longer time and later doesn't. The difference between these two queries is the single quote.
Does MYSQL treats these to query differently if so then why?
EDIT 1
I used EXPLAIN and got the following output but don't know how to interpret these two results.
mysql> EXPLAIN select * from AttDevice where phoneNumber = 5107357058;
+----+-------------+-----------+------+---------------------------------------+------+---------+------+---------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-----------+------+---------------------------------------+------+---------+------+---------+-------------+
| 1 | SIMPLE | Device | ALL | phoneNumber,idx_Device_phoneNumber | NULL | NULL | NULL | 6482116 | Using where |
+----+-------------+-----------+------+---------------------------------------+------+---------+------+---------+-------------+
1 row in set (0.00 sec)
mysql> EXPLAIN select * from AttDevice where phoneNumber = '5107357058';
+----+-------------+-----------+------+---------------------------------------+-------------+---------+-------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-----------+------+---------------------------------------+-------------+---------+-------+------+-------------+
| 1 | SIMPLE | Device | ref | phoneNumber,idx_Device_phoneNumber | phoneNumber | 258 | const | 2 | Using where |
+----+-------------+-----------+------+---------------------------------------+-------------+---------+-------+------+-------------+
1 row in set (0.00 sec)
Can someone explain me about the key, key_len and rows present in EXPLAIN query output?
1) Thank you for the "EXPLAIN". We all (including you, I'm sure) knew that the problem was that mysql had to convert the integer to a string, and had to do it for each row. But your "EXPLAIN" proved it.
2) Here's a nice, short article about EXPLAIN:
http://www.lornajane.net/posts/2011/explaining-mysqls-explain
The *possible_keys* shows which indexes apply to this query and the key
tells us which of those was actually used -... Finally the rows entry tell
us how many rows MySQL had to look at to find the result set.
Search value: key: type: ref: rows:
------------- --- ---- ---- ----
5107357058 NULL ALL NULL 6482116
'5107357058' phoneNumber ref const 2
3) The "ref" column is the "The columns compared to the index". In the second case, the string literal ("constant") '5107357058' was compared to the key "phoneNumber". In the first case, there was no usable key (because your search condition was a completely different type); hence "ref" was NULL.
4) The "type" column is "The join type". "Ref" means "All rows with matching index values are read from this table" (in this case, 2 rows). "ALL" mans "full table scan". Which in this case means 6 million rows.
5) Here's the mysql documentation for "EXPLAIN":
http://dev.mysql.com/doc/refman/5.5/en/explain-output.html
You fooled MySQL into making a bad choice by NOT quoting the phone number. Consider:
The column definition is varchar
In the first (unquoted) case you provided the value as an integer (long). I would have thought MySQL could figure this one out, but obviously it didn't, and did a full table scan.
In the second (quoted) case, you gave the search key in the correct datatype (character) and MySQL chose the index over the full-table-scan.
The varchar index cannot be used when you use a number as the operand, excerpt from the fine documentation on implicit type conversions:
For comparisons of a string column with a number, MySQL cannot use an index on the column to look up the value quickly. If str_col is an indexed string column, the index cannot be used when performing the lookup in the following statement:
SELECT * FROM tbl_name WHERE str_col=1;
The reason for this is that there are many different strings that may convert to the value 1, such as '1', ' 1', or '1a'.
I believe that MySQL has to convert the number into a varchar in the first example. In the second example it does not. I'm guessing that's where the difference is coming from.
The first example looks through the table one by one, the other one uses the index.
http://dev.mysql.com/doc/refman/5.0/en/show-columns.html
If Key is MUL, multiple occurrences of a given value are permitted within the column. The column is the first column of a nonunique index or a unique-valued index that can contain NULL values.
So instead of scanning all the null values, the second query look exclusively for for non-null values which speeds things up.
....I think.