In a very large MySQL analytics table - should I index the timestamp? - mysql

I'm looking to improve the speed of queries on a very large MySQL analytics table that I have. This table is tracking playercount on gameservers and the structure looks as so:
`server_tracker` (
`id` int(10) unsigned NOT NULL AUTO_INCREMENT,
`ip` int(10) unsigned NOT NULL,
`port` smallint(5) unsigned NOT NULL,
`date` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
`players` tinyint(3) unsigned NOT NULL,
`map` varchar(28) NOT NULL,
`portjoin` smallint(5) NOT NULL,
PRIMARY KEY (`id`),
KEY `idx_tracking_ip_port` (`ip`,`port`)
) ENGINE=InnoDB AUTO_INCREMENT=310729056 DEFAULT CHARSET=utf8 ROW_FORMAT=FIXED |
This table is inserted into very frequently, with 10k+ servers being tracked 10+ times an hour. However, every hour the data is taken and averaged out, and put into an "averaged" table with basically the same structure.
Currently I have the IP/port setup as key. However - sometimes it can be a tad slow when doing that hourly averaging - so I am curious if it would be worth putting an index on the timestamp, which is frequently used to select data from a certain timeframe like so:
SELECT `players`
FROM `server_tracker`
WHERE `ip` = x
AND `port` = x
AND `date` > NOW()
AND `date` < NOW() + INTERVAL 60 MINUTE
ORDER BY `id` DESC
This is the only type of query ran on this table. The table is only used for fetching the playercount from gameservers within a specific timeframe. The data is never updated or changed.
However, I am a bit new to all of this - and I am not sure if putting an index on the timestamp would do much of anything. Just looking for some friendly advice.
Results of EXPLAIN SELECT players FROM server_tracker WHERE ip = x AND port = x AND date > NOW() AND date < NOW() + INTERVAL 60 MINUTE ORDER BY id DESC
+----+-------------+-----------------+------+----------------------+----------------------+---------+-------------+-------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-----------------+------+----------------------+----------------------+---------+-------------+-------+-------------+
| 1 | SIMPLE | server_tracker | ref | idx_tracking_ip_port | idx_tracking_ip_port | 6 | const,const | 15354 | Using where |
+----+-------------+-----------------+------+----------------------+----------------------+---------+-------------+-------+-------------+

One of the most important information in MySQL and scripts is to know the MySQL to very few exceptions, always just ONE INDEX can be used in a query.
So it does not use much depending on an index ever to set a Column when all 4 are used verfelder in the where clause.
Only a combined index hilt over these fields.
The order of the fields is very important for this index can also be used for other queries.
An example:
An index on field1, field2 and field3 is used when you have the WHERE FIELD1 or FIELD1 and FIELD2 or field1, field2 and FIELD3. This index is not used if you in the WHERE FIELD2 or used FIELD3 or FIELD2 and field. 3 So always use the first field.
Too easy to find out if un like the QUERY works you can just run your query and EXPALIN and beommst directly the information whether and which index is used. If there are several lines you can as an indicator, the individual values ​​under rows muliplizieren together. The smaller this number is the better performs your query.
MariaDB [tmp]> EXPLAIN select * from content;
+------+-------------+---------+------+---------------+------+---------+------+------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+------+-------------+---------+------+---------------+------+---------+------+------+-------+
| 1 | SIMPLE | content | ALL | NULL | NULL | NULL | NULL | 13 | |
+------+-------------+---------+------+---------------+------+---------+------+------+-------+
1 row in set (0.00 sec)
MariaDB [tmp]>
Anternativ you can check out the profiler how long the QUERY in what capacity depends and about optimizing server
An example:
MariaDB [(none)]> use tmp
Database changed
MariaDB [tmp]> SET PROFILING=ON;
Query OK, 0 rows affected (0.00 sec)
MariaDB [tmp]>
MariaDB [tmp]> SELECT * FROM content;
+----+------+---------------------+--------+------+--------------+------+------+------+------+
| id | Wert | Zeitstempel | WertID | aaa | d | e | wwww | n | ddd |
+----+------+---------------------+--------+------+--------------+------+------+------+------+
| 1 | 10 | 2001-01-01 00:00:00 | 1 | NULL | 1.5000 | NULL | NULL | 1 | NULL |
| 2 | 12.3 | 2001-01-01 00:01:00 | 2 | NULL | 2.5000 | NULL | NULL | 2 | NULL |
| 3 | 17.4 | 2001-01-01 00:02:00 | 3 | NULL | 123456.1250 | NULL | NULL | 3 | NULL |
| 4 | 10.9 | 2001-01-01 01:01:00 | 1 | NULL | 1000000.0000 | NULL | NULL | 4 | NULL |
| 5 | 15.4 | 2001-01-01 01:02:00 | 2 | NULL | NULL | NULL | NULL | 5 | NULL |
| 6 | 20.9 | 2001-01-01 01:03:00 | 3 | NULL | NULL | NULL | NULL | 6 | NULL |
| 7 | 22 | 2001-01-02 00:00:00 | 1 | NULL | NULL | NULL | NULL | 7 | NULL |
| 8 | 12.3 | 2001-01-02 00:01:00 | 2 | NULL | NULL | NULL | NULL | 8 | NULL |
| 9 | 17.4 | 2001-01-02 00:02:00 | 3 | NULL | NULL | NULL | NULL |
+----+------+---------------------+--------+------+--------------+------+------+------+------+
13 rows in set (0.00 sec)
MariaDB [tmp]>
MariaDB [tmp]> SHOW PROFILE;
+----------------------+----------+
| Status | Duration |
+----------------------+----------+
| starting | 0.000031 |
| checking permissions | 0.000005 |
| Opening tables | 0.000036 |
| After opening tables | 0.000004 |
| System lock | 0.000003 |
| Table lock | 0.000002 |
| After opening tables | 0.000005 |
| init | 0.000013 |
| optimizing | 0.000006 |
| statistics | 0.000013 |
| preparing | 0.000010 |
| executing | 0.000002 |
| Sending data | 0.000073 |
| end | 0.000003 |
| query end | 0.000003 |
| closing tables | 0.000006 |
| freeing items | 0.000003 |
| updating status | 0.000012 |
| cleaning up | 0.000003 |
+----------------------+----------+
19 rows in set (0.00 sec)
MariaDB [tmp]>

Related

Performance problem in MySQL - Sum() and select is taking too long

i have a performance problem in MySQL.
I have a table with 215000 rows (InnoDB Engine) inserted in it and to execute the function SUM() on one column for only 1254 rows is taking 500ms.
The version i am using is : MySQL 5.7.32
The computer specs are the following:
Core I5 3.0 Ghz
8 Gb Ram
Solid State Drive
Here i leave information about the structure of the database:
mysql> select count(*) from cuenta_corriente;
+----------+
| count(*) |
+----------+
| 214514 |
+----------+
mysql> describe cuenta_corriente;
+-----------------------+-------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+-----------------------+-------------+------+-----+---------+----------------+
| ID_CUENTA_CORRIENTE | int(11) | NO | PRI | NULL | auto_increment |
| ID_CLIENTE | int(11) | YES | MUL | NULL | |
| ID_PROVEEDOR | int(11) | YES | MUL | NULL | |
| FECHA | varchar(50) | YES | MUL | NULL | |
| FECHA_FISCAL | varchar(50) | YES | | NULL | |
| ID_OPERACION | int(11) | YES | MUL | NULL | |
| ID_TIPO_OPERACION | int(11) | YES | MUL | NULL | |
| DEBE | double | YES | | 0 | |
| HABER | double | YES | | 0 | |
| TOTAL | double | YES | | 0 | |
| SALDO_ANTERIOR | double | YES | | 0 | |
| SALDO_ACTUAL | double | YES | | 0 | |
| ID_OPERACION_ASOCIADA | int(11) | YES | | NULL | |
| ELIMINADO | int(11) | YES | | 0 | |
| ID_EMPLEADO | int(11) | NO | | 0 | |
+-----------------------+-------------+------+-----+---------+----------------+
show indexes from cuenta_corriente;
+------------+------------------------------------------------------------+
| Non_unique | Key_name Column_name |
+------------+------------------------------------------------------------+
| 0 | PRIMARY ID_CUENTA_CORRIENTE |
| 1 | IDX_CUENTA_CORRIENTE ID_CLIENTE |
| 1 | IX_cuenta_corriente_FECHA FECHA |
| 1 | IX_cuenta_corriente_ID_CLIENTE ID_CLIENTE |
| 1 | IX_cuenta_corriente_ID_PROVEEDOR ID_PROVEEDOR |
| 1 | IX_cuenta_corriente_ID_TIPO_OPERACION ID_TIPO_OPERACION |
| 1 | IX_cuenta_corriente_ID_OPERACION ID_OPERACION |
| 1 | IDX_cuenta_corriente_ID_OPERACION ID_OPERACION |
+------------+------------------------------------------------------------+
8 rows in set (0.00 sec)
The problem is with the folowing queries, in my opinion they are taking too long, considering that i have an index for the column ID_CLIENTE and that there are only 1254 rows with the ID_CLIENTE column = 19. Here are the query results:
mysql> SELECT COUNT(*) FROM CUENTA_CORRIENTE WHERE ID_CLIENTE = 19;
1254 rows
mysql> SELECT DEBE FROM CUENTA_CORRIENTE WHERE ID_CLIENTE = 19;
1254 rows - 0.513 sec
mysql> SELECT SUM(DEBE) FROM CUENTA_CORRIENTE WHERE ID_CLIENTE = 19;
0.582 sec
The strange thing is if i select all the columns instead selecting only the "DEBE" column, it takes less time:
mysql> SELECT * FROM CUENTA_CORRIENTE WHERE ID_CLIENTE = 19;
0.095 sec
Can anyone help me to improve the performance?
You can make just that query fast by creating a composite index to support it.
ie:
CREATE INDEX IDX_QUERY_FAST ON cuenta_corriente (ID_CLIENTE, DEBE)
But don't forget, each index has to be maintained, so it slows down any inserts into the table, so you don't want 200 indexes supporting every possible query.
With the existing index, the engine should be smart enough to identify the 1200 rows you care about using the index, but then it has to go read all the table records (across however many pages) that have the individual rows to get the DEBE column.
Add this index to help most of the queries you have shown:
INDEX(ID_CLIENTE, DEBE)
and drop INDEX(ID_CLIENTE) if you have such.
Are all of your secondary indexes starting with the PRIMARY KEY?? (The names imply such; please provide SHOW CREATE TABLE to definitively say what columns are in each index.) Don't start an index with the PK; it is likely to be useless.
Run EXPLAIN SELECT ... to see which index a query uses.
When timing a query, run it twice. The first run may spend extra time loading index or data rows into cache (in RAM in the buffer_pool); the second run may be significantly faster because of the caching.

How to perform a sum for all previous records

I've been trying to implement the solution here with the added flavour of updating existing records. As an MRE I'm looking to populate the sum_date_diff column in a table with the sum of all the differences between the current row date and the date of every previous row where the current row p1_id matches the previous row p1_id or p2_id. I have already filled out the expected result below:
+-----+------------+-------+-------+---------------+
| id_ | date_time | p1_id | p2_id | sum_date_diff |
+-----+------------+-------+-------+---------------+
| 1 | 2000-01-01 | 1 | 2 | Null |
| 2 | 2000-01-02 | 2 | 4 | 1 |
| 3 | 2000-01-04 | 1 | 3 | 3 |
| 4 | 2000-01-07 | 2 | 5 | 11 |
| 5 | 2000-01-15 | 2 | 3 | 35 |
| 6 | 2000-01-20 | 1 | 3 | 35 |
| 7 | 2000-01-31 | 1 | 3 | 68 |
+-----+------------+-------+-------+---------------+
My query so far looks like:
UPDATE test.sum_date_diff AS sdd0
JOIN
(SELECT
id_,
SUM(DATEDIFF(sdd1.date_time, sq.date_time)) AS sum_date_diff
FROM
test.sum_date_diff AS sdd1
LEFT OUTER JOIN (SELECT
sdd2.date_time AS date_time, sdd2.p1_id AS player_id
FROM
test.sum_date_diff AS sdd2 UNION ALL SELECT
sdd3.date_time AS date_time, sdd3.p2_id AS player_id
FROM
test.sum_date_diff AS sdd3) AS sq ON sq.date_time < sdd1.date_time
AND sq.player_id = sdd1.p1_id
GROUP BY sdd1.id_) AS master_sq ON master_sq.id_ = sdd0.id_
SET
sdd0.sum_date_diff = master_sq.sum_date_diff
This works as shown here.
However, on a table of 1.5m records the query has been hanging for the last hour. Even when I add a WHERE clause onto the bottom to restrict the update to a single record then it hangs for 5 mins+.
Here is the EXPLAIN statement for the query on the full table:
+----+-------------+---------------+------------+-------+-----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------+---------+-------+---------+----------+--------------------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+---------------+------------+-------+-----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------+---------+-------+---------+----------+--------------------------------------------+
| 1 | UPDATE | sum_date_diff | NULL | const | PRIMARY | PRIMARY | 4 | const | 1 | 100 | NULL |
| 1 | PRIMARY | <derived2> | NULL | ref | <auto_key0> | <auto_key0> | 4 | const | 10 | 100 | NULL |
| 2 | DERIVED | sum_date_diff | NULL | index | PRIMARY,ix__match_oc_history__date_time,ix__match_oc_history__p1_id,ix__match_oc_history__p2_id,ix__match_oc_history__date_time_players | ix__match_oc_history__date_time_players | 14 | NULL | 1484288 | 100 | Using index; Using temporary |
| 2 | DERIVED | <derived3> | NULL | ALL | NULL | NULL | NULL | NULL | 2968576 | 100 | Using where; Using join buffer (hash join) |
| 3 | DERIVED | sum_date_diff | NULL | index | NULL | ix__match_oc_history__date_time_players | 14 | NULL | 1484288 | 100 | Using index |
| 4 | UNION | sum_date_diff | NULL | index | NULL | ix__match_oc_history__date_time_players | 14 | NULL | 1484288 | 100 | Using index |
+----+-------------+---------------+------------+-------+-----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------+---------+-------+---------+----------+--------------------------------------------+
Here is the CREATE TABLE statement:
CREATE TABLE `sum_date_diff` (
`id_` int NOT NULL AUTO_INCREMENT,
`date_time` datetime DEFAULT NULL,
`p1_id` int NOT NULL,
`p2_id` int NOT NULL,
`sum_date_diff` int DEFAULT NULL,
PRIMARY KEY (`id_`),
KEY `ix__sum_date_diff__date_time` (`date_time`),
KEY `ix__sum_date_diff__p1_id` (`p1_id`),
KEY `ix__sum_date_diff__p2_id` (`p2_id`),
KEY `ix__sum_date_diff__date_time_players` (`date_time`,`p1_id`,`p2_id`)
) ENGINE=InnoDB AUTO_INCREMENT=1822120 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci
MySQL version is 8.0.26 running on a 2016 MacBook Pro with Monterey with 16Gb RAM.
After reading around about boosting the RAM available to MySQL I've added the following to the standard my.cnf file:
innodb_buffer_pool_size = 8G
tmp_table_size=2G
max_heap_table_size=2G
I'm wondering if:
I've done something wrong
This is just a very slow task no matter what I do
There is a faster method
I'm hoping someone could enlighten me!
Whereas it is possible to do calculations like this in SQL, it is messy. If the number of rows is not in the millions, I would fetch the necessary columns into my application and do the arithmetic there. (Loops are easier and faster in PHP/Java/etc than in SQL.)
LEAD() and LAG() are possible, but they are not optimized well (or so is my experience). In an APP language, it is easy and efficient to look up things in arrays.
The SELECT can (easily and efficiently) do any filtering and sorting so that the app only receives the necessary data.

Optimizing join on derived table - EXPLAIN different on local and server

I have the following ugly query, which runs okay but not great, on my local machine (1.4 secs, running v5.7). On the server I'm using, which is running an older version of MySQL (v5.5), the query just hangs. It seems to get caught on "Copying to tmp table":
SELECT
SQL_CALC_FOUND_ROWS
DISTINCT p.parcel_number,
p.street_number,
p.street_name,
p.site_address_city_state,
p.number_of_units,
p.number_of_stories,
p.bedrooms,
p.bathrooms,
p.lot_area_sqft,
p.cost_per_sq_ft,
p.year_built,
p.sales_date,
p.sales_price,
p.id
FROM (
SELECT APN, property_case_detail_id FROM property_inspection AS pi
GROUP BY APN, property_case_detail_id
HAVING
COUNT(IF(status='Resolved Date', 1, NULL)) = 0
) as open_cases
JOIN property AS p
ON p.parcel_number = open_cases.APN
LIMIT 0, 1000;
mysql> show processlist;
+-------+-------------+-----------+--------------+---------+------+----------------------+------------------------------------------------------------------------------------------------------+
| Id | User | Host | db | Command | Time | State | Info |
+-------+-------------+-----------+--------------+---------+------+----------------------+------------------------------------------------------------------------------------------------------+
| 21120 | headsupcity | localhost | lead_housing | Query | 21 | Copying to tmp table | SELECT
SQL_CALC_FOUND_ROWS
DISTINCT p.parcel_number,
p.street_numbe |
| 21121 | headsupcity | localhost | lead_housing | Query | 0 | NULL | show processlist |
+-------+-------------+-----------+--------------+---------+------+----------------------+------------------------------------------------------------------------------------------------------+
2 rows in set (0.00 sec)
Explains are different on my local machine and on the server, and I'm assuming the only reason my query runs at all on my local machine, is because of the key that is automatically created on the derived table:
Explain (local):
+----+-------------+------------+------------+------+---------------+-------------+---------+------------------------------+---------+----------+---------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+------------+------------+------+---------------+-------------+---------+------------------------------+---------+----------+---------------------------------+
| 1 | PRIMARY | p | NULL | ALL | NULL | NULL | NULL | NULL | 40319 | 100.00 | Using temporary |
| 1 | PRIMARY | <derived2> | NULL | ref | <auto_key0> | <auto_key0> | 8 | lead_housing.p.parcel_number | 40 | 100.00 | NULL |
| 2 | DERIVED | pi | NULL | ALL | NULL | NULL | NULL | NULL | 1623978 | 100.00 | Using temporary; Using filesort |
+----+-------------+------------+------------+------+---------------+-------------+---------+------------------------------+---------+----------+---------------------------------+
Explain (server):
+----+-------------+------------+------+---------------+------+---------+------+---------+------------------------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+------------+------+---------------+------+---------+------+---------+------------------------------------------+
| 1 | PRIMARY | p | ALL | NULL | NULL | NULL | NULL | 41369 | Using temporary |
| 1 | PRIMARY | <derived2> | ALL | NULL | NULL | NULL | NULL | 122948 | Using where; Distinct; Using join buffer |
| 2 | DERIVED | pi | ALL | NULL | NULL | NULL | NULL | 1718586 | Using temporary; Using filesort |
+----+-------------+------------+------+---------------+------+---------+------+---------+------------------------------------------+
Schemas:
mysql> explain property_inspection;
+-------------------------+--------------+------+-----+-------------------+-----------------------------+
| Field | Type | Null | Key | Default | Extra |
+-------------------------+--------------+------+-----+-------------------+-----------------------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| lblCaseNo | int(11) | NO | MUL | NULL | |
| APN | bigint(10) | NO | MUL | NULL | |
| date | varchar(50) | NO | | NULL | |
| status | varchar(500) | NO | | NULL | |
| property_case_detail_id | int(11) | YES | MUL | NULL | |
| case_type_id | int(11) | YES | MUL | NULL | |
| date_modified | timestamp | NO | | CURRENT_TIMESTAMP | on update CURRENT_TIMESTAMP |
| update_status | tinyint(1) | YES | | 1 | |
| created_date | datetime | NO | | NULL | |
+-------------------------+--------------+------+-----+-------------------+-----------------------------+
10 rows in set (0.02 sec)
mysql> explain property; (not all columns, but you get the gist)
+----------------------------+--------------+------+-----+-------------------+-----------------------------+
| Field | Type | Null | Key | Default | Extra |
+----------------------------+--------------+------+-----+-------------------+-----------------------------+
| id | int(11) | NO | PRI | NULL | auto_increment |
| parcel_number | bigint(10) | NO | | 0 | |
| date_modified | timestamp | NO | | CURRENT_TIMESTAMP | on update CURRENT_TIMESTAMP |
| created_date | datetime | NO | | NULL | |
+----------------------------+--------------+------+-----+-------------------+-----------------------------+
Variables that might be relevant:
tmp_table_size: 16777216
innodb_buffer_pool_size: 8589934592
Any ideas on how to optimize this, and any idea why the explains are so different?
Since this is where the Optimizers are quite different, let's try to optimize
SELECT APN, property_case_detail_id FROM property_inspection AS pi
GROUP BY APN, property_case_detail_id
HAVING
COUNT(IF(status='Resolved Date', 1, NULL)) = 0
) as open_cases
Give this a try:
SELECT ...
FROM property AS p
WHERE NOT EXISTS ( SELECT 1 FROM property_inspection
WHERE status = 'Resolved Date'
AND p.parcel_number = APN )
ORDER BY ??? -- without this, the `LIMIT` is unpredictable
LIMIT 0, 1000;
or...
SELECT ...
FROM property AS p
LEFT JOIN property_inspection AS pi ON p.parcel_number = pi.APN
WHERE pi.status = 'Resolved Date'
AND pi.APN IS NULL
ORDER BY ??? -- without this, the `LIMIT` is unpredictable
LIMIT 0, 1000;
Index:
property_inspection: INDEX(status, parcel_number) -- in either order
MySQL 5.5 and 5.7 are quite different and the later has better optimizer so there is no surprise that explain plans are different.
You'd better provide SHOW CREATE TABLE property; and SHOW CREATE TABLE property_inspection; outputs as it will show indexes that are on your tables.
Your sub-query is the issue.
- Server tries to process 1.6M rows with no index and grouping everything.
- Having is quite expensive operation so you'd better avoid it, expecially in sub-queries.
- Grouping in this case is bad idea. You do not need the aggregation/counting. You need to check if the 'Resolved Date' status is just exists
Based on the information provided I'd recommend:
- Alter table property_inspection to reduce length of status column.
- Add index on the column. Use covering index (APN, property_case_detail_id, status) if possible (in this columns order).
- Change query to something like this:
SELECT
SQL_CALC_FOUND_ROWS
DISTINCT p.parcel_number,
...
p.id
FROM
property_inspection AS `pi1`
INNER JOIN property AS p ON (
p.parcel_number = `pi1`.APN
)
LEFT JOIN (
SELECT
`pi2`.property_case_detail_id
, `pi2`. APN
FROM
property_inspection AS `pi2`
WHERE
`status` = 'Resolved Date'
) AS exclude ON (
exclude.APN = `pi1`.APN
AND exclude.property_case_detail_id = `pi1`.property_case_detail_id
)
WHERE
exclude.APN IS NULL
LIMIT
0, 1000;

mysql query running too slow for discontinuous data

I am new to MySQL, and trying to using MySQL on the project, basically was tracking players performance.
Below is the table fields.
+-------------------+----------------------+-------------------+------+-----+---------+----------------+---------------------------------+---------+
| Field | Type | Collation | Null | Key | Default | Extra | Privileges | Comment |
+-------------------+----------------------+-------------------+------+-----+---------+----------------+---------------------------------+---------+
| unique_id | int(11) | NULL | NO | PRI | NULL | auto_increment | select,insert,update,references | |
| record_time | datetime | NULL | NO | | NULL | | select,insert,update,references | |
| game_sourceid | char(20) | latin1_swedish_ci | NO | MUL | NULL | | select,insert,update,references | |
| game_number | smallint(6) | NULL | NO | | NULL | | select,insert,update,references | |
| game_difficulty | char(12) | latin1_swedish_ci | NO | MUL | NULL | | select,insert,update,references | |
| cost_time | smallint(5) unsigned | NULL | NO | MUL | NULL | | select,insert,update,references | |
| country | char(3) | latin1_swedish_ci | NO | | NULL | | select,insert,update,references | |
| source | char(7) | latin1_swedish_ci | NO | | NULL | | select,insert,update,references | |
+-------------------+----------------------+-------------------+------+-----+---------+----------------+---------------------------------+---------+
and I have adding game_sourceid and game_difficulty as index and the engine is innodb.
I have insert about 11m rows of test data into this table, which is generated randomly but resembles the real data.
Basically the mostly query was like this, to get the average time and best time for a specific game_sourceid
SELECT avg(cost_time) AS avgtime
, min(cost_time) AS mintime
, count(*) AS count
FROM statistics_work_table
WHERE game_sourceid = 'standard_easy_1';
+-----------+---------+--------+
| avgtime | mintime | count |
+-----------+---------+--------+
| 1681.2851 | 420 | 138034 |
+-----------+---------+--------+
1 row in set (4.97 sec)
and the query took about 5s
I have googled about this and someone said that may caused by the amout of query count, so I am trying to narrow down the scope like this
SELECT avg(cost_time) AS avgtime
, min(cost_time) AS mintime
, count(*) AS count
FROM statistics_work_table
WHERE game_sourceid = 'standard_easy_1'
AND record_time > '2015-11-19 04:40:00';
+-----------+---------+-------+
| avgtime | mintime | count |
+-----------+---------+-------+
| 1275.2222 | 214 | 9 |
+-----------+---------+-------+
1 row in set (4.46 sec)
As you can see the 9 rows data also took about 5s, so i think it's not the problem about the query count.
The test data was generated randomly to simulate the real user's activity, so the data was discontinuous, so i added more continuous data(about 250k) with the same game_sourceid='standard_easy_9' but keep all others randomly, in other words the last 250k rows in this table has the same game_sourceid. And i'm trying to query like this:
SELECT avg(cost_time) AS avgtime
, min(cost_time) AS mintime
, count(*) AS count
FROM statistics_work_table
WHERE game_sourceid = 'standard_easy_9';
+-----------+---------+--------+
| avgtime | mintime | count |
+-----------+---------+--------+
| 1271.4806 | 70 | 259379 |
+-----------+---------+--------+
1 row in set (0.40 sec)
This time the query magically took only 0.4s, that's totally beyond my expectations.
So here's the question, the data was retrived from the player at real time, so it must be randomly and discontinuous.
I am thinking of separating the data into multiple tables by the game_sourceid, but it will take another 80 tables for that, maybe more in the future.
Since I am new to MySQL, I am wondering if there are any other solutions for this, or just my query was too bad.
Update: Here's the index of my table
mysql> show index from statistics_work_table;
+-----------------------+------------+-------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-----------------------+------------+-------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| statistics_work_table | 0 | PRIMARY | 1 | unique_id | A | 11362113 | NULL | NULL | | BTREE | | |
| statistics_work_table | 1 | GameSourceId_CostTime | 1 | game_sourceid | A | 18 | NULL | NULL | | BTREE | | |
| statistics_work_table | 1 | GameSourceId_CostTime | 2 | cost_time | A | 344306 | NULL | NULL | | BTREE | | |
+-----------------------+------------+-------------------------+--------------+-----------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
ALTER TABLE `statistics_work_table`
ADD INDEX `GameSourceId_CostTime` (`game_sourceid`,`cost_time`)
This index should make your queries super fast. Also, after you run the above statement, you should drop the single column index you have on game_sourceid, as the above will make the single column one redundant. (Which will hurt insert speed.)
The reason your queries are slow is because the database is using your single column index on game_sourceid, finding the rows, and then, for each row, using the primary key that is stored along with the index to find the main clustered index (aka primary key in this, and most cases), and then looking up the cost_time value. This is referred to as a double lookup, and it is something you want to avoid.
The index I provided above is called a "covering index". It allows your query to use ONLY the index, and so you only need a single lookup per row, greatly improving performance.

How can I optimize this mysql query to find maximum simultaneous calls?

I'm trying to calculate maximum simultaneous calls. My query, which I believe to be accurate, takes way too long given ~250,000 rows. The cdrs table looks like this:
+---------------+-----------------------+------+-----+---------+----------------+
| Field | Type | Null | Key | Default | Extra |
+---------------+-----------------------+------+-----+---------+----------------+
| id | bigint(20) unsigned | NO | PRI | NULL | auto_increment |
| CallType | varchar(32) | NO | | NULL | |
| StartTime | datetime | NO | MUL | NULL | |
| StopTime | datetime | NO | | NULL | |
| CallDuration | float(10,5) | NO | | NULL | |
| BillDuration | mediumint(8) unsigned | NO | | NULL | |
| CallMinimum | tinyint(3) unsigned | NO | | NULL | |
| CallIncrement | tinyint(3) unsigned | NO | | NULL | |
| BasePrice | float(12,9) | NO | | NULL | |
| CallPrice | float(12,9) | NO | | NULL | |
| TransactionId | varchar(20) | NO | | NULL | |
| CustomerIP | varchar(15) | NO | | NULL | |
| ANI | varchar(20) | NO | | NULL | |
| ANIState | varchar(10) | NO | | NULL | |
| DNIS | varchar(20) | NO | | NULL | |
| LRN | varchar(20) | NO | | NULL | |
| DNISState | varchar(10) | NO | | NULL | |
| DNISLATA | varchar(10) | NO | | NULL | |
| DNISOCN | varchar(10) | NO | | NULL | |
| OrigTier | varchar(10) | NO | | NULL | |
| TermRateDeck | varchar(20) | NO | | NULL | |
+---------------+-----------------------+------+-----+---------+----------------+
I have the following indexes:
+-------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| cdrs | 0 | PRIMARY | 1 | id | A | 269622 | NULL | NULL | | BTREE | | |
| cdrs | 1 | id | 1 | id | A | 269622 | NULL | NULL | | BTREE | | |
| cdrs | 1 | call_time_index | 1 | StartTime | A | 269622 | NULL | NULL | | BTREE | | |
| cdrs | 1 | call_time_index | 2 | StopTime | A | 269622 | NULL | NULL | | BTREE | | |
+-------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
The query I am running is this:
SELECT MAX(cnt) AS max_channels FROM
(SELECT cl1.StartTime, COUNT(*) AS cnt
FROM cdrs cl1
INNER JOIN cdrs cl2
ON cl1.StartTime
BETWEEN cl2.StartTime AND cl2.StopTime
GROUP BY cl1.id)
AS counts;
It seems like I might have to chunk this data for each day and store the results in a separate table like simultaneous_calls.
I'm sure you want to know not only the maximum simultaneous calls, but when that happened.
I would create a table containing the timestamp of every individual minute
CREATE TABLE times (ts DATETIME UNSIGNED AUTO_INCREMENT PRIMARY KEY);
INSERT INTO times (ts) VALUES ('2014-05-14 00:00:00');
. . . until 1440 rows, one for each minute . . .
Then join that to the calls.
SELECT ts, COUNT(*) AS count FROM times
JOIN cdrs ON times.ts BETWEEN cdrs.starttime AND cdrs.stoptime
GROUP BY ts ORDER BY count DESC LIMIT 1;
Here's the result in my test (MySQL 5.6.17 on a Linux VM running on a Macbook Pro):
+---------------------+----------+
| ts | count(*) |
+---------------------+----------+
| 2014-05-14 10:59:00 | 1001 |
+---------------------+----------+
1 row in set (1 min 3.90 sec)
This achieves several goals:
Reduces the number of rows examined by two orders of magnitude.
Reduces the execution time from 3 hours+ to about 1 minute.
Also returns the actual timestamp when the highest count was found.
Here's the EXPLAIN for my query:
explain select ts, count(*) from times join cdrs on times.ts between cdrs.starttime and cdrs.stoptime group by ts order by count(*) desc limit 1;
+----+-------------+-------+-------+---------------+---------+---------+------+--------+------------------------------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+-------+---------------+---------+---------+------+--------+------------------------------------------------+
| 1 | SIMPLE | times | index | PRIMARY | PRIMARY | 5 | NULL | 1440 | Using index; Using temporary; Using filesort |
| 1 | SIMPLE | cdrs | ALL | starttime | NULL | NULL | NULL | 260727 | Range checked for each record (index map: 0x4) |
+----+-------------+-------+-------+---------------+---------+---------+------+--------+------------------------------------------------+
Notice the figures in the rows column, and compare to the EXPLAIN of your original query. You can estimate the total number of rows examined by multiplying these together (but that gets more complicated if your query is anything other than SIMPLE).
The inline view isn't strictly necessary. (You're right about a lot of time to run the EXPLAIN on the query with the inline view, the EXPLAIN will materialize the inline view (i.e. run the inline view query and populate the derived table), and then give an EXPLAIN on the outer query.
Note that this query will return an equivalent result:
SELECT COUNT(*) AS max_channels
FROM cdrs cl1
JOIN cdrs cl2
ON cl1.StartTime BETWEEN cl2.StartTime AND cl2.StopTime
GROUP BY cl1.id
ORDER BY max_channels DESC
LIMIT 1
Though it still has to do all the work, and probably doesn't perform any better; the EXPLAIN should run a lot faster. (We expect to see "Using temporary; Using filesort" in the Extra column.)
The number of rows in the resultset is going to be the number of rows in the table (~250,000 rows), and those are going to need to be sorted, so that's going to be some time there. The bigger issue (my gut is telling me) is that join operation.
I'm wondering if the EXPLAIN (or performance) would be any different if you swapped the cl1 and cl2 in the predicate, i.e.
ON cl2.StartTime BETWEEN cl1.StartTime AND cl1.StopTime
I'm thinking that, just because I'd be tempted to try a correlated subquery. That's ~250,000 executions, and that's not likely going to be any faster...
SELECT ( SELECT COUNT(*)
FROM cdrs cl2
WHERE cl2.StartTime BETWEEN cl1.StartTime AND cl1.StopTime
) AS max_channels
, cl1.StartTime
FROM cdrs cl1
ORDER BY max_channels DESC
LIMIT 11
You could run an EXPLAIN on that, we're still going to see a "Using temporary; Using filesort", and it will also show the "dependent subquery"...
Obviously, adding a predicate on the cl1 table to cut down the number of rows to be returned (for example, checking only the past 15 days); that should speed things up, but it doesn't get you the answer you want.
WHERE cl1.StartTime > NOW() - INTERVAL 15 DAY
(None of my musings here are sure-fire answers to your question, or solutions to the performance issue; they're just musings.)