I am collecting about 3 - 6 millions lines of stock data per day and storing it in a MySQL database.
All of the data is coming from Interactive Brokers every piece of information comes with these five fields: Symbol, Date, Time, Value and Type (type being information on what type of data I am receiving such as price, volume etc)
Here is my create table statement. idticks is just my unique key but I almost never am able to use it in queries.
CREATE TABLE `ticks` (
`idticks` int(11) NOT NULL AUTO_INCREMENT,
`symbol` varchar(30) NOT NULL,
`date` int(11) NOT NULL,
`time` int(11) NOT NULL,
`value` double NOT NULL,
`type` double NOT NULL,
KEY `idticks` (`idticks`),
KEY `symbol` (`symbol`),
KEY `date` (`date`),
KEY `idx_ticks_symbol_date` (`symbol`,`date`),
KEY `idx_ticks_type` (`type`),
KEY `idx_ticks_date_type` (`date`,`type`),
KEY `idx_ticks_date_symbol_type` (`date`,`symbol`,`type`),
KEY `idx_ticks_symbol_date_time_type` (`symbol`,`date`,`time`,`type`)
) ENGINE=InnoDB AUTO_INCREMENT=13533258 DEFAULT CHARSET=utf8
/*!50100 PARTITION BY KEY (`date`)
PARTITIONS 1 */;
As you can see, I have no idea what I am doing because I just keep on creating indexes to make my queries go faster.
Right now the data is being stored on a rather slow computer for testing purposes so I understand that my queries are not nearly as fast as they could be (I have a 6 core, 64gig of ram, SSD machine arriving tomorrow which should help significantly)
That being said, I am running queries like this one
select time, value from ticks where symbol = "AAPL" AND date = 20150522 and type = 8 order by time asc
The query above, if I do not limit it, returns 12928 records for one of my test days and takes 10.2 seconds if I do it from cleared cache.
I am doing lots of graphing and eventually would like to be able to just query the data as I need to it graph. Right now I haven't noticed a lot of difference in speed between getting part of a days worth of data vs just getting the entire day's. It would be cool to have those queries respond fast enough that there is barely any delay when I moving to the next day/screen whatever.
Another query I am using for usability of a program I am writing to interact with the data include
String query = "select distinct `date` from ticks where symbol = '" + symbol + "' order by `date` desc";
But most of my need is the ability to pull a certain type of data from a certain day for a certain symbol like my first query.
I've googled all over the place and I think I understand that creating tons of indexes makes the database bigger and slows down the input speed (I get about 300 pieces of information per second on a busy day). Should I just index each column individually?
I am willing to throw more harddrives at things if it means responsive interface.
Basically, my questions relate to the creation/altering of my table. Based on the above query, can you think of anything I could do to make that faster? Or an indexing system that would help me out? Is InnoDB even the right engine? I tried googling this vs MyISam and after a couple of hours of this, I still wasn't sure.
Thanks :)
Combine date and time into a DATETIME field
Assuming Price and Volume always come in together, put them together (2 columns) and get rid if type.
Get rid of the AUTO_INCREMENT; change to PRIMARY KEY(symbol, datetime)
Get rid of any indexes that are the left part of some other index.
Once you are using DATETIME, use date ranges to find everything in a single date (if you need such). Do not use DATE(datetime) = '...', performance will be terrible.
Symbol can probably be ascii, not utf8.
Use InnoDB, the clustering of the Primary Key can be beneficial.
Do you expect to collect (and use) more data than will fit in innodb_buffer_pool_size? If so, we need to discuss your SELECTs and look into PARTITIONing.
Make those changes, then come back for more advice/abuse.
You're creating a historical database, so MyISAM would work as well as InnoDB. InnoDB is a transactional relational database, and is better suited for relational databases with multiple tables that must remain synchronized.
Your Stock table looks like this.
Stock
-----
Stock ID (idticks)
Symbol
Date
Time
Value
Type
It would be better if you combine the date and time into a time stamp column, and unpack the types like this.
Stock
-----
Stock ID
Symbol
Time Stamp
Volume
Open
Close
Bid
Ask
...
This makes it easier for the database to return rows for a query on a particular type, like the close value.
As far as indexes, you can create as many indexes as you want. You're adding (inserting) information, so the increased time to add information is offset by the decreased time to query the information.
I'd have a primary index on Stock ID, and a unique index on Symbol and Time Stamp descending. You could also have indexes on the values you query most often, like Close.
Related
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.
I am having difficulties in optimizing this SQL statement in MySQL. I have two tables that are populated independently and so the times logged in each table's column will not be the same. What I want is a single table (view) that lists all the records in the sensor_history with the current process information that was present at the sensor's measurement_time. If a process log time was not present, I can live with NULLs in the process fields in the resulting view for that particular record.
What I have here works but it is brute force and woefully inefficient. There are about 500k records in the sensor_history table and about 20k records in the process_history table. I have tried getting my head around different join methods but I run into syntax issues or bad results. I have tried some online optimizers without success and so I am hoping someone here can point me in the right direction.
For simplicity, I have removed the foreign key relations to other tables. There are no indices in use but feel free to suggest any that may help. Here are the basics:
CREATE TABLE `sensor_history` (
`measurement_time_utc` int(11) NOT NULL,
`sensor_id` int(11) NOT NULL,
`sensor_measurement_x` double NOT NULL,
`sensor_measurement_y` double NOT NULL,
`sensor_measurement_z` double NOT NULL,
`sensor_quality` int(11) NOT NULL
);
CREATE TABLE `process_history` (
`log_time_utc` int(11) NOT NULL,
`process_id` int(11) NOT NULL,
`process_speed` double NOT NULL,
`process_load` int(11) NOT NULL
);
CREATE VIEW `rollup` AS SELECT
`sensor_history`.`measurement_time_utc`,
`sensor_history`.`sensor_id`,
`sensor_history`.`sensor_measurement_x`,
`sensor_history`.`sensor_measurement_y`,
`sensor_history`.`sensor_measurement_z`,
`sensor_history`.`sensor_quality`,
(SELECT `process_history`.`process_id` FROM `process_history` WHERE `sensor_history`.`measurement_time_utc`>=`process_history`.`log_time_utc` ORDER BY `process_history`.`log_time_utc` DESC LIMIT 1) AS `process_id`,
(SELECT `process_history`.`process_speed` FROM `process_history` WHERE `sensor_history`.`measurement_time_utc`>=`process_history`.`log_time_utc` ORDER BY `process_history`.`log_time_utc` DESC LIMIT 1) AS `process_speed`,
(SELECT `process_history`.`process_load` FROM `process_history` WHERE `sensor_history`.`measurement_time_utc`>=`process_history`.`log_time_utc` ORDER BY `process_history`.`log_time_utc` DESC LIMIT 1) AS `process_load`
FROM `sensor_history`;
How can I make a more efficient rollup view? Thanks in advance.
Views are really hard to optimize in MySQL. Your best hope is for an index on:
process_history(log_time_utc, process_id, process_speed)
The last two columns are included so the index covers the query and doesn't need to refer to the data pages.
While you are trying to figure out what the Analysts really need, let's do some improvements that are easier to do now than later.
DOUBLE takes 8 bytes and delivers about 16 significant digits. That is gross overkill for every sensor I have heard of. Consider the 4-byte FLOAT, which gives you about 7 significant digits.
(Where am I going with this? Capturing "sensor" data keeps coming, and it eventually fills up disk and that makes it slow. So, let's shrink things soon.)
INT is 4 bytes and has a range of +/- 2 billion. Are you expecting that many sensors? How about a 1-byte TINYINT UNSIGNED with a range of 0..255? Or `SMALLINT UNSIGNED (1-bytes, range 0..64K)? Ditto for any other ids.
Or... Do you really need to save all the data? Maybe day-old data can be summarized down to hourly min, max, avg, etc? And month-old data is needed only to a day's resolution?
We have lots to discuss once your analysts explain to you what the do want. Then you need to read-between-the-lines to see what they will want. (I can help there, too.)
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.
The application we are developing is writing around 4-5 millions rows of data every day. And, we need to save these data for the past 90 days.
The table user_data has the following structure (simplified):
id INT PRIMARY AUTOINCREMENT
dt TIMESTAMP CURRENT_TIMESTAMP
user_id varchar(20)
data varchar(20)
About the application:
Data that is older than 7 days old will not be written / updated.
Data is mostly accessed based on user_id (i.e. all queries will have WHERE user_id = XXX)
There are around 13000 users at the moment.
User can still access older data. But, in accessing the older data, we can restrict that he/she can only get the whole day data only and not a time range. (e.g. If a user attempts to get the data for 2016-10-01, he/she will get the data for the whole day and will not be able to get the data for 2016-10-01 13:00 - 2016-10-01 14:00).
At the moment, we are using MySQL InnoDB to store the latest data (i.e. 7 days and newer) and it is working fine and fits in the innodb_buffer_pool.
As for the older data, we created smaller tables in the form of user_data_YYYYMMDD. After a while, we figured that these tables cannot fit into the innodb_buffer_pool and it started to slow down.
We think that separating / sharding based on dates, sharding based on user_ids would be better (i.e. using smaller data sets based on user and dates such as user_data_[YYYYMMDD]_[USER_ID]). This will keep the table in much smaller numbers (only around 10K rows at most).
After researching around, we have found that there are a few options out there:
Using mysql tables to store per user per date (i.e. user_data_[YYYYMMDD]_[USER_ID]).
Using mongodb collection for each user_data_[YYYYMMDD]_[USER_ID]
Write the old data (json encoded) into [USER_ID]/[YYYYMMDD].txt
The biggest con I see in this is that we will have huge number of tables/collections/files when we do this (i.e. 13000 x 90 = 1.170.000). I wonder if we are approaching this the right way in terms of future scalability. Or, if there are other standardized solutions for this.
Scaling a database is an unique problem to the application. Most of the times someone else's approach cannot be used as almost all applications writes its data in its own way. So you have to figure out how you are going to manage your data.
Having said that, if your data continue to grow, best solution is the shadring where you can distribute the data across different servers. As long as bound to a single server like creating different tables you are getting hit by resource limits like memory, storage and processing power. Those cannot be increased unlimited manner.
How to distribute the data, that you have to figure out based on your business use cases. As you mentioned, if you are not getting more request on old data, the best way to distribute the data base on date. Like DB for 2016 data, DB for 2015 and so on. Later you may purge or shutdown the servers which you have more old data.
This is a big table, but not unmanageable.
If user_id + dt is UNIQUE, make it the PRIMARY KEY, and get rid if id, thereby saving space. (More in a minute...)
Normalize user_id to a SMALLINT UNSIGNED (2 bytes) or, to be safer MEDIUMINT UNSIGNED (3 bytes). This will save a significant amount of space.
Saving space is important for speed (I/O) for big tables.
PARTITION BY RANGE(TO_DAYS(dt))
with 92 partitions -- the 90 you need, plus 1 waiting to be DROPped and one being filled. See details here .
ENGINE=InnoDB
to get the PRIMARY KEY clustered.
PRIMARY KEY(user_id, dt)
If this is "unique", then it allows efficient access for any time range for a single user. Note: you can remove the "just a day" restriction. However, you must formulate the query without hiding dt in a function. I recommend:
WHERE user_id = ?
AND dt >= ?
AND dt < ? + INTERVAL 1 DAY
Furthermore,
PRIMARY KEY(user_id, dt, id),
INDEX(id)
Would also be efficient even if (user_id, dt) is not unique. The addition of id to the PK is to make it unique; the addition of INDEX(id) is to keep AUTO_INCREMENT happy. (No, UNIQUE(id) is not required.)
INT --> BIGINT UNSIGNED ??
INT (which is SIGNED) will top out at about 2 billion. That will happen in a very few years. Is that OK? If not, you may need BIGINT (8 bytes vs 4).
This partitioning design does not care about your 7-day rule. You may choose to keep the rule and enforce it in your app.
BY HASH
will not work as well.
SUBPARTITION
is generally useless.
Are there other queries? If so they must be taken into consideration at the same time.
Sharding by user_id would be useful if the traffic were too much for a single server. MySQL, itself, does not (yet) have a sharding solution.
Try TokuDB engine at https://www.percona.com/software/mysql-database/percona-tokudb
Archive data are great for TokuDB. You will need about six times less disk space to store AND memory to PROCESS your dataset compared to InnoDB or about 2-3 times less than archived myisam.
1 million+ tables sounds like a bad idea. Having sharding via dynamic table naming by the app code at runtime has also not been a favorable pattern for me. My first go-to for this type of problem would be partitioning. You probably don't want 400M+ rows in a single unpartitioned table. In MySQL 5.7 you can even subpartition (but that gets more complex). I would first range partition on your date field, with one partition per day. Index on the user_id. If you are on 5.7 and want to dabble with subpartitioning, I would suggest range partition by date, then hash subpartition by user_id. As a starting point, try 16 to 32 hash buckets. Still index the user_id field.
EDIT: Here's something to play with:
CREATE TABLE user_data (
id INT AUTO_INCREMENT
, dt TIMESTAMP DEFAULT CURRENT_TIMESTAMP
, user_id VARCHAR(20)
, data varchar(20)
, PRIMARY KEY (id, user_id, dt)
, KEY (user_id, dt)
) PARTITION BY RANGE (UNIX_TIMESTAMP(dt))
SUBPARTITION BY KEY (user_id)
SUBPARTITIONS 16 (
PARTITION p1 VALUES LESS THAN (UNIX_TIMESTAMP('2016-10-25')),
PARTITION p2 VALUES LESS THAN (UNIX_TIMESTAMP('2016-10-26')),
PARTITION p3 VALUES LESS THAN (UNIX_TIMESTAMP('2016-10-27')),
PARTITION p4 VALUES LESS THAN (UNIX_TIMESTAMP('2016-10-28')),
PARTITION pMax VALUES LESS THAN MAXVALUE
);
-- View the metadata if you're interested
SELECT * FROM information_schema.partitions WHERE table_name='user_data';
I have a need for a solution that would allow me to track every single click (and the link clicked, and the date) in a web application (PHP5 / MySQL5.7). The simplest solution is obviously a simple table :
CREATE TABLE stats_data (
id INT NOT NULL PRIMARY KEY AUTO_INCREMENT,
log_date DATETIME NOT NULL DEFAULT NOW(),
link VARCHAR(512) NOT NULL
)
I'm not such how this scales up performance-wise, as the expected amount of clicks per day could well go above 10000.
Is this a reliable solution, say, after 5 months of data stored ?
What optimizations could make this solution perform better?
If not, what would be a better solution approach for this ?
Mostly it depends on your use-case. What queries do you want to run over this dataset?
I would definitely recommend some document oriented database (like Redis or MongoDb), but as I said, it depends how will you use your data.
If you want to stick to MySQL, I have some advice on how to make that solution more reliable.
Dont save every click into database each time is clicked, but store it into cache (memcached for example) and once every hour save into MySQL
Make own table for each month to not make searches in one large table. And backup that table each month.
I guess you could possible put the links in a separate table and have your table reference that as a foreign key. Should possible make it faster to for example check the number of clicks on a specific link.
Depending on how accurate you want the data you could also aggregate it into another table in maby a nightly running operation of some sort (scheduled sp should work).
That way you can have a table where you for example can se how many times a link was clicked in a specific interval, a day or an hour or whatever suits your needs. I've used this approach at work where we store statistic data on web-service calls in an application with very heavy load and it has been working fine with no performance issues what so ever.
There's a couple of thinks you can do to ensure performance:
Index log_date column so queries can run faster when searching for results by dates range (https://dev.mysql.com/doc/refman/5.5/en/column-indexes.html)
Create partitions by log_date column (https://dev.mysql.com/doc/refman/5.6/en/partitioning-types.html)
By partitioning data by date columns you can "separate" data by hour / day / week / month / year... whatever you want...
Example:
CREATE TABLE members (
firstname VARCHAR(25) NOT NULL,
lastname VARCHAR(25) NOT NULL,
username VARCHAR(16) NOT NULL,
email VARCHAR(35),
joined DATE NOT NULL
)
PARTITION BY RANGE( YEAR(joined) ) (
PARTITION p0 VALUES LESS THAN (1960),
PARTITION p1 VALUES LESS THAN (1970),
PARTITION p2 VALUES LESS THAN (1980),
PARTITION p3 VALUES LESS THAN (1990),
PARTITION p4 VALUES LESS THAN MAXVALUE
)
Therefore, imagining that you separates data by week, when you search by a log with date equal to '2016-08-25', that record will be searched only on logs with dates between '2016-08-22' and '2016-08-28'.
I hope this can help you.