This is a theoretical question. Sorry, but I don't have a working tables data to show, I'll try to improvise with a theoretical example.
Using MySql/MariaDB. Have indexes for all relevant fields.
I have a system, which historical design had a ProductType table, something like:
ID=1, Description="Milk"
ID=2, Description="Bread"
ID=3, Description="Salt"
ID=4, Description="Sugar"
and so on.
There are some features in the system that rely on the ProductType ID and the Description is also used in different places, such as for defining different properties of the product type.
There is also a Product table, with fields such as:
ID, ProductTypeID, Name
The Product:Name don't have the product type description in it, so a "Milk bottle 1l" will have an entry such as:
ID=101, ProductTypeID=1, Name="bottle 1l"
and "Sugar pack 1kg" will be:
ID=102, ProductTypeID=4, Name="pack 1kg"
You get the idea...
The system combines the ProductType:Description and Product:Name to show full product names to the users. This creates a systematic naming for all the products, so there is no way to define a product with a name such as "1l bottle of milk". I know that in English that might be hard to swallow, but that way works great with my local language.
Years passed, the database grow to millions of products.
Since full-text index should have all searched data in one table, I had to store the ProductType:Description inside the Product table in a string field I added that have different keywords related to the product, so the full-text search will be able to find anything related to the product (type, name, barcode, SKU and etc.)
Now I'm trying to solve the full table scans and it makes me think that current design might not be optimal and I'll have to redesign and store the full product name (type + name) in the same table...
In order to show the proper order of the products there's an ORDER BY TypeDescription ASC, ProductName ASC after the ProductType table is joined to Product select queries.
From my research I see that the database can't use indexes when the order is done on fields from different tables, so it's doing full table scan to get to the right entries.
During pagination, there's ORDER and LIMIT 50000,100 in the query that take lots of time.
There are sections with lots for products, so that ordering and limiting cause very long full table scans.
How would you handle that situation?
Change the design and store all query related data to the Product table? Feels a bit of a duplication and not natural solution.
Or maybe there's another way to solve it?
Will index on VARCHAR type (product name) be efficient for the ORDER speed? Or the database will still do full table scan?
My first question here. Couldn't find answers on similar cases.
Thanks!
I've tried to play with the queries to see if ordering by a VARCHAR field that have an index will work, but the EXPLAIN SELECT still shows that the query didn't use the index and did WHERE run :(
UPDATE
Trying to add some more data...
The situation is a bit more complicated and after digging a bit more it looks like the initial question was not in the right direction.
I removed the product type from the queries and still have the slow query.
I feel like it's a chicken and egg situation...
I have a table that maps prodcut IDs to section IDs:
CREATE TABLE `Product2Section` (
`SectionId` int(10) unsigned NOT NULL,
`ProductId` int(10) unsigned NOT NULL,
KEY `idx_ProductId` (`ProductId`),
KEY `idx_SectionId` (`SectionId`),
KEY `idx_ProductId_SectionId` (`ProductId`,`SectionId`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 ROW_FORMAT=DYNAMIC
The query (after stripping all non-relevant to the question feilds):
SELECT DISTINCT
DRIVER.ProductId AS ID,
p.*
FROM
Product2Section AS DRIVER
LEFT JOIN Product p ON
(p.ID = DRIVER.ProductId)
WHERE
DRIVER.SectionId IN(
544,545,546,548,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,566,567,568,570,571,572,573,574,575,1337,1343,1353,1358,1369,1385,1956,1957,1964,1973,1979,1980,1987,1988,1994,1999,2016,2020,576,577,578,579,580,582,586,587,589,590,591,593,596,597,598,604,605,606,608,609,612,613,614,615,617,619,620,621,622,624,625,626,627,628,629,630,632,634,635,637,639,640,642,643,644,645,647,648,651,656,659,660,661,662,663,665,667,669,670,672,674,675,677,683,684,689,690,691,695,726,728,729,730,731,734,736,741,742,743,745,746,749,752,758,761,762,763,764,768,769,771,772,773,774,775,776,777
)
ORDER BY
p.ProductName ASC
LIMIT 500900,100;
explain shows:
id
select_type
table
type
possible_keys
key
key_len
ref
rows
Extra
1
SIMPLE
DRIVER
index
idx_SectionId
idx_ProductId_SectionId
8
NULL
589966
Using where; Using index; Using temporary; Using filesort
1
SIMPLE
p
eq_ref
PRIMARY,idx_ID
PRIMARY
4
4project.DRIVER.ProductId
1
Using where
I've tried to select from the products table and join the Product2Section in order to filter the results, but get the same results:
SELECT DISTINCT
p.ID,
p.ProductName
FROM
Product p
LEFT JOIN
Product2Section p2s ON (p.ID=p2s.ProductId)
WHERE
p2s.SectionId IN(
544,545,546,548,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,566,567,568,570,571,572,573,574,575,1337,1343,1353,1358,1369,1385,1956,1957,1964,1973,1979,1980,1987,1988,1994,1999,2016,2020,576,577,578,579,580,582,586,587,589,590,591,593,596,597,598,604,605,606,608,609,612,613,614,615,617,619,620,621,622,624,625,626,627,628,629,630,632,634,635,637,639,640,642,643,644,645,647,648,651,656,659,660,661,662,663,665,667,669,670,672,674,675,677,683,684,689,690,691,695,726,728,729,730,731,734,736,741,742,743,745,746,749,752,758,761,762,763,764,768,769,771,772,773,774,775,776,777
)
ORDER BY
p.ProductName ASC
LIMIT 500900,
100;
explain:
id
select_type
table
type
possible_keys
key
key_len
ref
rows
Extra
1
SIMPLE
p2s
index
idx_ProductId,idx_SectionId,idx_ProductId_SectionId
idx_ProductId_SectionId
8
NULL
589966
Using where; Using index; Using temporary; Using filesort
1
SIMPLE
p
eq_ref
PRIMARY,idx_ID
PRIMARY
4
4project.p2s.ProductId
1
Using where
Don't see a way out of that situation.
The two single column indices on Product2Section serve no purpose. You should change your junction table to:
CREATE TABLE `Product2Section` (
`SectionId` int unsigned NOT NULL,
`ProductId` int unsigned NOT NULL,
PRIMARY KEY (`SectionId`, `ProductId`),
KEY `idx_ProductId_SectionId` (`ProductId`, `SectionId`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
There are other queries in the system that probably use the single field indexes
The single column indices cannot be used for anything that the two composite indices cannot be used for. They are just wasting space and cause unnecessary overhead on insert and for the optimizer. Setting one of the composite indices as PRIMARY stops InnoDB from having to create its own internal rowid, which just wastes space. It also adds the uniqueness constraint which is currently missing from your table.
From the docs:
Accessing a row through the clustered index is fast because the index search leads directly to the page that contains the row data. If a table is large, the clustered index architecture often saves a disk I/O operation when compared to storage organizations that store row data using a different page from the index record.
This is not significant for a "simple" junction table as both columns should be stored in both indices, therefor no further read is required.
You said:
that didn't really bother me since there was no real performance hit
You may not see the difference when running an individual query with no contention but the difference in a highly contended production environment can be huge, due to the amount of effort required.
Do you really need to accommodate 4,294,967,295 (int unsigned) sections? Perhaps the 65,535 provided by smallint unsigned would be enough?
You said:
Might change it in the future. Don't think it will change the performance somehow
Changing SectionId to smallint will reduce each index entry from 8 to 6 bytes. That's a 25% reduction in size. Smaller is faster.
Why are you using LEFT JOIN? The fact that you are happy to reverse the order of the tables in the query suggests it should be an INNER JOIN.
Do you have your buffer pool configured appropriately, or is it set to defaults? Please run ANALYZE TABLE Product2Section; and then provide the output from:
SELECT TABLE_ROWS, AVG_ROW_LENGTH, DATA_LENGTH + INDEX_LENGTH
FROM information_schema.TABLES
WHERE TABLE_NAME = 'Product2Section';
And:
SELECT ROUND(SUM(DATA_LENGTH + INDEX_LENGTH)/POW(1024, 3), 2)
FROM information_schema.TABLES
WHERE TABLE_SCHEMA = 'your_database_name';
And:
SHOW VARIABLES LIKE 'innodb_buffer%';
Hi I currently have a query which is taking 11(sec) to run. I have a report which is displayed on a website which runs 4 different queries which are similar and all take 11(sec) each to run. I don't really want the customer having to wait a minute for all of these queries to run and display the data.
I am using 4 different AJAX requests to call an APIs to get the data I need and these all start at once but the queries are running one after another. If there was a way to get these queries to all run at once (parallel) so the total load time is only 11(sec) that would also fix my issue, I don't believe that is possible though.
Here is the query I am running:
SELECT device_uuid,
day_epoch,
is_repeat
FROM tracking_daily_stats_zone_unique_device_uuids_per_hour
WHERE day_epoch >= 1552435200
AND day_epoch < 1553040000
AND venue_id = 46
AND zone_id IN (102,105,108,110,111,113,116,117,118,121,287)
I can't think of anyway to speed this query up at all, below are pictures of the table indexes and the explain statement on this query.
I think the above query is using relevant indexes in the where conditions.
If there is anything you can think of to speed this query up please let me know, I have been working on it for 3 days and can't seem to figure out the problem. It would be great to get the query times down to 5(sec) maximum. If I am wrong about the AJAX issue please let me know as this would also fix my issue.
" EDIT "
I have came across something quite strange which might be causing the issue. When I change the day_epoch range to something smaller (5th - 9th) which returns 130,000 rows the query time is 0.7(sec) but then I add one more day onto that range (5th - 10th) and it returns over 150,000 rows the query time is 13(sec). I have ran loads of different ranges and have came to the conclusion if the amount of rows returned is over 150,000 that has a huge effect on the query times.
Table Definition -
CREATE TABLE `tracking_daily_stats_zone_unique_device_uuids_per_hour` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`day_epoch` int(10) NOT NULL,
`day_of_week` tinyint(1) NOT NULL COMMENT 'day of week, monday = 1',
`hour` int(2) NOT NULL,
`venue_id` int(5) NOT NULL,
`zone_id` int(5) NOT NULL,
`device_uuid` binary(16) NOT NULL COMMENT 'binary representation of the device_uuid, unique for a single day',
`device_vendor_id` int(5) unsigned NOT NULL DEFAULT '0' COMMENT 'id of the device vendor',
`first_seen` int(10) unsigned NOT NULL DEFAULT '0',
`last_seen` int(10) unsigned NOT NULL DEFAULT '0',
`is_repeat` tinyint(1) NOT NULL COMMENT 'is the device a repeat for this day?',
`prev_last_seen` int(10) NOT NULL DEFAULT '0' COMMENT 'previous last seen ts',
PRIMARY KEY (`id`,`venue_id`) USING BTREE,
KEY `venue_id` (`venue_id`),
KEY `zone_id` (`zone_id`),
KEY `day_of_week` (`day_of_week`),
KEY `day_epoch` (`day_epoch`),
KEY `hour` (`hour`),
KEY `device_uuid` (`device_uuid`),
KEY `is_repeat` (`is_repeat`),
KEY `device_vendor_id` (`device_vendor_id`)
) ENGINE=InnoDB AUTO_INCREMENT=450967720 DEFAULT CHARSET=utf8
/*!50100 PARTITION BY HASH (venue_id)
PARTITIONS 100 */
The straight forward solution is to add this query specific index to the table:
ALTER TABLE tracking_daily_stats_zone_unique_device_uuids_per_hour
ADD INDEX complex_idx (`venue_id`, `day_epoch`, `zone_id`)
WARNING This query change can take a while on DB.
And then force it when you call:
SELECT device_uuid,
day_epoch,
is_repeat
FROM tracking_daily_stats_zone_unique_device_uuids_per_hour
USE INDEX (complex_idx)
WHERE day_epoch >= 1552435200
AND day_epoch < 1553040000
AND venue_id = 46
AND zone_id IN (102,105,108,110,111,113,116,117,118,121,287)
It is definitely not universal but should work for this particular query.
UPDATE When you have partitioned table you can get profit by forcing particular PARTITION. In our case since that is venue_id just force it:
SELECT device_uuid,
day_epoch,
is_repeat
FROM tracking_daily_stats_zone_unique_device_uuids_per_hour
PARTITION (`p46`)
WHERE day_epoch >= 1552435200
AND day_epoch < 1553040000
AND zone_id IN (102,105,108,110,111,113,116,117,118,121,287)
Where p46 is concatenated string of p and venue_id = 46
And another trick if you go this way. You can remove AND venue_id = 46 from WHERE clause. Because there is no other data in that partition.
What happens if you change the order of conditions? Put venue_id = ? first. The order matters.
Now it first checks all rows for:
- day_epoch >= 1552435200
- then, the remaining set for day_epoch < 1553040000
- then, the remaining set for venue_id = 46
- then, the remaining set for zone_id IN (102,105,108,110,111,113,116,117,118,121,287)
When working with heavy queries, you should always try to make the first "selector" the most effective. You can do that by using a proper index for 1 (or combination) index and to make sure that first selector narrows down the most (at least for integers, in case of strings you need another tactic).
Sometimes, a query simply is slow. When you have a lot of data (and/or not enough resources) you just cant really do anything about that. Thats where you need another solution: Make a summary table. I doubt you show 150.000 rows x4 to your visitor. You can sum it, e.g., hourly or every few minutes and select from that way smaller table.
Offtopic: Putting an index on everything only slows you down when inserting/updating/deleting. Index the least amount of columns, just the once you actually filter on (e.g. use in a WHERE or GROUP BY).
450M rows is rather large. So, I will discuss a variety of issues that can help.
Shrink data A big table leads to more I/O, which is the main performance killer. ('Small' tables tend to stay cached, and not have an I/O burden.)
Any kind of INT, even INT(2) takes 4 bytes. An "hour" can easily fit in a 1-byte TINYINT. That saves over a 1GB in the data, plus a similar amount in INDEX(hour).
If hour and day_of_week can be derived, don't bother having them as separate columns. This will save more space.
Some reason to use a 4-byte day_epoch instead of a 3-byte DATE? Or perhaps you do need a 5-byte DATETIME or TIMESTAMP.
Optimal INDEX (take #1)
If it is always a single venue_id, then either this is a good first cut at the optimal index:
INDEX(venue_id, zone_id, day_epoch)
First is the constant, then the IN, then a range. The Optimizer does well with this in many cases. (It is unclear whether the number of items in an IN clause can lead to inefficiencies.)
Better Primary Key (better index)
With AUTO_INCREMENT, there is probably no good reason to include columns after the auto_inc column in the PK. That is, PRIMARY KEY(id, venue_id) is no better than PRIMARY KEY(id).
InnoDB orders the data's BTree according to the PRIMARY KEY. So, if you are fetching several rows and can arrange for them to be adjacent to each other based on the PK, you get extra performance. (cf "Clustered".) So:
PRIMARY KEY(venue_id, zone_id, day_epoch, -- this order, as discussed above;
id) -- to make sure that the entire PK is unique.
INDEX(id) -- to keep AUTO_INCREMENT happy
And, I agree with DROPping any indexes that are not in use, including the one I recommended above. It is rarely useful to index flags (is_repeat).
UUID
Indexing a UUID can be deadly for performance once the table is really big. This is because of the randomness of UUIDs/GUIDs, leading to ever-increasing I/O burden to insert new entries in the index.
Multi-dimensional
Assuming day_epoch is sometimes multiple days, you seem to have 2 or 3 "dimensions":
A date range
A list of zones
A venue.
INDEXes are 1-dimensional. Therein lies the problem. However, PARTITIONing can sometimes help. I discuss this briefly as "case 2" in http://mysql.rjweb.org/doc.php/partitionmaint .
There is no good way to get 3 dimensions, so let's focus on 2.
You should partition on something that is a "range", such as day_epoch or zone_id.
After that, you should decide what to put in the PRIMARY KEY so that you can further take advantage of "clustering".
Plan A: This assumes you are searching for only one venue_id at a time:
PARTITION BY RANGE(day_epoch) -- see note below
PRIMARY KEY(venue_id, zone_id, id)
Plan B: This assumes you sometimes srefineearch for venue_id IN (.., .., ...), hence it does not make a good first column for the PK:
Well, I don't have good advice here; so let's go with Plan A.
The RANGE expression must be numeric. Your day_epoch works fine as is. Changing to a DATE, would necessitate BY RANGE(TO_DAYS(...)), which works fine.
You should limit the number of partitions to 50. (The 81 mentioned above is not bad.) The problem is that "lots" of partitions introduces different inefficiencies; "too few" partitions leads to "why bother".
Note that almost always the optimal PK is different for a partitioned table than the equivalent non-partitioned table.
Note that I disagree with partitioning on venue_id since it is so easy to put that column at the start of the PK instead.
Analysis
Assuming you search for a single venue_id and use my suggested partitioning & PK, here's how the SELECT performs:
Filter on the date range. This is likely to limit the activity to a single partition.
Drill into the data's BTree for that one partition to find the one venue_id.
Hopscotch through the data from there, landing on the desired zone_ids.
For each, further filter based the date.
example i have some gps devices that send info to my database every seconds
so 1 device create 1 row in mysql database with these columns (8)
id=12341 date=22.02.2018 time=22:40
langitude=22.236558789 longitude=78.9654582 deviceID=24 name=device-name someinfo=asdadadasd
so for 1 minute it create 60 rows , for 24 hours it create 864000 rows
and for 1 month(31days) 2678400 ROWS
so 1 device is creating 2.6 million rows per month in my db table ( records are deleted every month.)
so if there are more devices will be 2.6 Million * number of devices
so my questions are like this:
Question 1: if i make a search like this from php ( just for current day and for 1 device)
SELECT * FROM TABLE WHERE date='22.02.2018' AND deviceID= '24'
max possible results will be 86400 rows
will it overload my server too much
Question 2: limit with 5 hours (18000 rows) will that be problem for database or will it load server like first example or less
SELECT * FROM TABLE WHERE date='22.02.2018' AND deviceID= '24' LIMIT 18000
Question 3: if i show just 1 result from db will it overload server
SELECT * FROM TABLE WHERE date='22.02.2018' AND deviceID= '24' LIMIT 1
does it mean that if i have millions of rows and 1000rows will load server same if i show just 1 result
Millions of rows is not a problem, this is what SQL databases are designed to handle, if you have a well designed schema and good indexes.
Use proper types
Instead of storing your dates and times as separate strings, store them either as a single datetime or separate date and time types. See indexing below for more about which one to use. This is both more compact, allows indexing, faster sorting, and it makes available date and time functions without having to do conversions.
Similarly, be sure to use the appropriate numeric type for latitude, and longitude. You'll probably want to use numeric to ensure precision.
Since you're going to be storing billions of rows, be sure to use a bigint for your primary key. A regular int can only go up to about 2 billion.
Move repeated data into another table.
Instead of storing information about the device in every row, store that in a separate table. Then only store the device's ID in your log. This will cut down on your storage size, and eliminate mistakes due to data duplication. Be sure to declare the device ID as a foreign key, this will provide referential integrity and an index.
Add indexes
Indexes are what allows a database to search through millions or billions of rows very, very efficiently. Be sure there are indexes on the rows you use frequently, such as your timestamp.
A lack of indexes on date and deviceID is likely why your queries are so slow. Without an index, MySQL has to look at every row in the database known as a full table scan. This is why your queries are so slow, you're lacking indexes.
You can discover whether your queries are using indexes with explain.
datetime or time + date?
Normally it's best to store your date and time in a single column, conventionally called created_at. Then you can use date to get just the date part like so.
select *
from gps_logs
where date(created_at) = '2018-07-14'
There's a problem. The problem is how indexes work... or don't. Because of the function call, where date(created_at) = '2018-07-14' will not use an index. MySQL will run date(created_at) on every single row. This means a performance killing full table scan.
You can work around this by working with just the datetime column. This will use an index and be efficient.
select *
from gps_logs
where '2018-07-14 00:00:00' <= created_at and created_at < '2018-07-15 00:00:00'
Or you can split your single datetime column into date and time columns, but this introduces new problems. Querying ranges which cross a day boundary becomes difficult. Like maybe you want a day in a different time zone. It's easy with a single column.
select *
from gps_logs
where '2018-07-12 10:00:00' <= created_at and created_at < '2018-07-13 10:00:00'
But it's more involved with a separate date and time.
select *
from gps_logs
where (created_date = '2018-07-12' and created_time >= '10:00:00')
or (created_date = '2018-07-13' and created_time < '10:00:00');
Or you can switch to a database with partial indexes like Postgresql. A partial index allows you to index only part of a value, or the result of a function. And Postgresql does a lot of things better than MySQL. This is what I recommend.
Do as much work in SQL as possible.
For example, if you want to know how many log entries there are per device per day, rather than pulling all the rows out and calculating them yourself, you'd use group by to group them by device and day.
select gps_device_id, count(id) as num_entries, created_at::date as day
from gps_logs
group by gps_device_id, day;
gps_device_id | num_entries | day
---------------+-------------+------------
1 | 29310 | 2018-07-12
2 | 23923 | 2018-07-11
2 | 23988 | 2018-07-12
With this much data, you will want to rely heavily on group by and the associated aggregate functions like sum, count, max, min and so on.
Avoid select *
If you must retrieve 86400 rows, the cost of simply fetching all that data from the database can be costly. You can speed this up significantly by only fetching the columns you need. This means using select only, the, specific, columns, you, need rather than select *.
Putting it all together.
In PostgreSQL
Your schema in PostgreSQL should look something like this.
create table gps_devices (
id serial primary key,
name text not null
-- any other columns about the devices
);
create table gps_logs (
id bigserial primary key,
gps_device_id int references gps_devices(id),
created_at timestamp not null default current_timestamp,
latitude numeric(12,9) not null,
longitude numeric(12,9) not null
);
create index timestamp_and_device on gps_logs(created_at, gps_device_id);
create index date_and_device on gps_logs((created_at::date), gps_device_id);
A query can generally only use one index per table. Since you'll be searching on the timestamp and device ID together a lot timestamp_and_device combines indexing both the timestamp and device ID.
date_and_device is the same thing, but it's a partial index on just the date part of the timestamp. This will make where created_at::date = '2018-07-12' and gps_device_id = 42 very efficient.
In MySQL
create table gps_devices (
id int primary key auto_increment,
name text not null
-- any other columns about the devices
);
create table gps_logs (
id bigint primary key auto_increment,
gps_device_id int references gps_devices(id),
foreign key (gps_device_id) references gps_devices(id),
created_at timestamp not null default current_timestamp,
latitude numeric(12,9) not null,
longitude numeric(12,9) not null
);
create index timestamp_and_device on gps_logs(created_at, gps_device_id);
Very similar, but no partial index. So you'll either need to always use a bare created_at in your where clauses, or switch to separate date and time types.
Just read you question, for me the Answer is
Just create a separate table for Latitude and longitude and make your ID Foreign key and save it their.
Without knowing the exact queries you want to run I can just guess the best structure. Having said that, you should aim for the optimal types that use the minimum number of bytes per row. This should make your queries faster.
For example, you could use the structure below:
create table device (
id int primary key not null,
name varchar(20),
someinfo varchar(100)
);
create table location (
device_id int not null,
recorded_at timestamp not null,
latitude double not null, -- instead of varchar; maybe float?
longitude double not null, -- instead of varchar; maybe float?
foreign key (device_id) references device (id)
);
create index ix_loc_dev on location (device_id, recorded_at);
If you include the exact queries (naming the columns) we can create better indexes for them.
Since probably your query selectivity is bad, your queries may run Full Table Scans. For this case I took it a step further I used the smallest possible data types for the columns, so it will be faster:
create table location (
device_id tinyint not null,
recorded_at timestamp not null,
latitude float not null,
longitude float not null,
foreign key (device_id) references device (id)
);
Can't really think of anything smaller than this.
The best what I can recommend to you is to use time-series database for storing and accessing time-series data. You can host any kind of time-series database engine locally, just put a little bit more resources into development of it's access methods or use any specialized databases for telematics data like this.