Im in the process of developing a large scale application that will contain a few tables with a large dataset. (Potentially 1M+ rows). This application will be a game with multiple users completing tasks at the same time and will be very data intensive.
In this application, data will be aggregated for users statistics. I have came up with two scenarios to achieve my desired affect of calculating all the statistics.
Scenario 1
Maintain a separate table to calculate user statistics. Meaning as a move is processed, the field would increase by one.
Table Statistics (Moves, Origins, Points)
$Moves++;
$Origins++
$Points = $Points + $Points;
Scenario 2
Count and sum the data fields as needed across all data.
Table Moves (Points, Origins)
SUM(Points)
SUM(Origins)
COUNT(Moves)
My question is, which of these two scenarios would be the most efficient on the database driver. It is my belief that Scenario 2 could possibly be more efficient because there will be far less data manipulation, but I'm unsure of the load that these queries may place on the DB.
I am using MySQL 5.5 InnoDB with a UTF8 Charset
The best route will depend on the frequency of reads vs. writes of points, origins and moves. Those frequencies, in turn, will be dependent upon use cases, code style and use (or lack) of caching.
It's difficult to provide a qualified opinion without more details, but consider the fact that a dedicated table brings with it some additional complications in the way of additional writes necessary for each operation and ensuring that those data tallies must always be correct (match the underlying detail data). In light of the additional complication storing logical data elements once rather than twice in a relational database is usually the best course of action.
If you're worried about performance and scaleability you might want to consider a non-relational approach using database platforms like Mongo or DynamoDB.
Related
I have an Oracle database view in which I have access to 17 columns and approximately 15k rows (this grows at a rate of about 700 rows per year). I only need to use 10 of the columns. At the moment I am searching for ways to make my query more efficient since my app load about 7.5k of the entries at first. I know I could only load lets say 1k entries and that would be a way to speed up the loading process; however, the users often need to query through more than the 1k entries loaded initially, and I do not want to make them wait through a second loading of data into the app.
So I guess my main question is that when I query the Oracle view should I query and just do a * query on the database or select specific columns? I know that best practices state only query the columns you need; however, I am looking at this from a performance standpoint and would I see a significant performance increase by only querying the 10 specific columns I need rather than a * query on the view?
As #AndyLester says, the only way to know for sure is to try it out and see. There are reasons to expect that specifying the actual set of columns you need will be faster. The question is whether the difference will be "significant" which is something only you can tell us.
There are a few reasons to expect performance improvements
Specifying the actual set of columns decreases the amount of data that has to be transmitted over the network and decreases the amount of memory that is consumed on the client. Whether this is significant or not depends on the relative size of the columns that you're selecting vs. the columns you're excluding. If you only need a bunch of varchar2(10) columns and the columns that you don't need include some varchar2(1000) columns, you might be eliminating the vast majority of your network traffic and of the RAM consumed on the client. If you're only excluding a few char(1) columns while you're selecting a bunch of clob columns, the reduction may be trivial.
Specifying the actual set of columns can produce a more efficient plan. Depending on the Oracle version, the view definition, and the definition of the underlying tables it's possible that some of the joins can be eliminated when you're selecting a subset of columns. This, in turn, can produce a much more efficient plan.
Specifying the actual set of columns means that your application's performance is much less likely to change if additional columns are added to the view. Your code won't suddenly start pulling that new data over the network into memory structures on the client. It may not need to join in the additional tables that might be referenced.
Since there is no downside to specifying the column list, I'd strongly suggest doing so regardless of the size of the performance improvement. If you're really concerned about performance, however, it's likely that you'd want to be looking at performance more holistically (examining what is actually taking time in your process, for example).
Simplified scenario:
I have a table with about 100,000 rows.
I will need to pick about 300-400 rows, based on certain criteria, to display them on a web page.
Considering the above scenario, which one of the below approaches will you recommend?
Approach 1: Use just one database query to select the entire table into one big array of 100,000 rows. Using loops, pick required 300-400 rows from the array and pass it one to the front-end. Minimum load on the database server, as it's just one query. Put's more load on the PHP, as it has to store and search through an array of 100,000.
Approach 2: Using a loop, PHP will generate a new query for each row of required data. Collecting all the data will require 300-400 independent queries. More load on the server. Compared to approach 1, lesser load on PHP.
Opinions / thoughts will be appreciated!
100,000 rows is a small amount for MySQL rdbms.
You would better do fine tuning of the db server.
So I recommend neither 1 nor 2.
Just:
SELECT * FROM `your_table` WHERE `any_field` = 'YOUR CRITERIA' LIMIT 300;
When your data overcomes 1,000,000 rows you should think about strong indexes optimization and maybe you'll have to create a stored procedure for complicated select. I assure you it's not PHP work in any case.
As your question asks from Performance prospective, your both approaches would consume some resources. I would still go for approach 1 in this case, as it doesn't make query to database again and again, if you generate query for each row i.e. 300-400 queries. When it comes to huge project designing, database always comes as bottleneck.
To be honest, both approaches are not good. Its good practice to have good database design and query selection. What you are trying to achieve could be done by suitable query.
Using PHP to loop through the data is really a bad idea, after all, a database is designed to perform queries. PHP will need to loop through all the record, and doesn't use an index to speed things up; this is roughly equivalent to a 'table scan' in the database.
In order to get the most performance out of your database, it's important to have a good design and (for example) create indexes on the right columns.
Also, if you haven't decided yet what RDBMS you're going to use, depending on your usage, some databases have more advanced options that can assist in better performance (e.g. PostgreSQL has support for geographical information)
Pease provide some actual data (what kind of data will be stored, what kind of fields) and samples of the kind of queries / filters that will need to be performed so that people will be able to give you an actual answer, not a hypothetical
I'm doing an RIA with JavaScript, MySQL and PHP on a Windows server.
I have 5,000 identically structured data sets I want to put in a database. 5 tables is enough for the data, all of which will be reasonably small except for one table that will have 300,000+ records for a typical data set.
Additionally, 500 users will get read only access to statistics compiled from those data sets. Those statistics are provided by PHP (no direct access is allowed). What's more, their access to data varies. Some users can only use one data set, others some, a few, all.
The results users see are relatively small; most requests return well under 100 rows, and the largest requests will be about 700 rows. All requests are through a JavaScript RIA which uses Ajax to connect to PHP which in turn connects to the data, does its thing and outputs JSON in response, which JavaScript then presents accordingly.
In thinking about how to structure this, three options present themselves:
Put the data sets in the same tables. That could easily give me 1,500,000,000 records in the largest table.
Use separate tables for each data set. That would limit the largest table size, but could mean 25,000 tables.
Forget the database and stick with the proprietary format.
I'm leaning towards #2 for a few reasons.
I'm concerned about issues in using very large tables (eg: query speeds, implementation limits, etc...).
Separate tables seem safer; they limit the impact of errors and structure changes.
Separate tables allow me to use MySQL's table level security rather than implementing my own row level security. This means less work and better protection; for instance, if a query is accidentally sent without row level security, users can get unauthorized data. Not so with table level security, as the database will reject the query out of hand.
Those are my thoughts, but I'd like yours. Do you think this is the right choice? If not, why not? What considerations have I missed? Should I consider other platforms if scale-ability is an issue?
1) I'm concerned about issues in using very large tables (eg: query speeds, implementation limits, etc...).
Whether the DBMS has to...
search through the large index of one table,
or search for the right table and then search through the smaller index of that table
...probably doesn't make much of a difference performance-wise. If anything, the second case has an undocumented component (the performance of locating the right table), so I'd be reluctant to trust it fully.
If you want to physically partition the data, MySQL supports that directly since version 5.1, so you don't have to emulate it via separate tables.
2) Separate tables seem safer; they limit the impact of errors and structure changes.
That's what backups are for.
3) Separate tables allow me to use MySQL's table level security rather than implementing my own row level security.
True enough, however similar effect can be achieved through views or stored procedures.
All in all, my instinct is to go with a single table, unless you know in advance that these data-sets differ enough structurally to warrant separate tables. BTW, I doubt you'd be able to do better with a proprietary format compared to a well-optimized database.
Let's pretend with me here:
PHP/MySQL web-application. Assume a single server and a single MySQL DB.
I have 1,000 bosses. Every boss has 10 workers under them. These 10 workers (times 1k, totaling 10,000 workers) each have at least 5 database entries (call them work orders for this purpose) in the WebApplication every work day. That's 50k entries a day in this work orders table.
Server issues aside, I see two main ways to handle the basic logic of the database here:
Each Boss has an ID. There is one table called workorders and it has a column named BossID to associate every work order with a boss. This leaves you with approximately 1 million entries a month in a single table, and to me that seems to add up fast.
Each Boss has it's own table that is created when that Boss signed up, i.e. work_bossID where bossID = the boss' unique ID. This leaves you with 1,000 tables, but these tables are much more manageable.
Is there a third option that I'm overlooking?
Which method would be the better-functioning method?
How big is too big for number of entries in a table (let's assume a small number of columns: less than 10)? (this can include: it's time to get a second server when...)
How big is too big for number of tables in a database? (this can include: it's time to get a second server when...)
I know that at some point we have to bring in talks of multiple servers, and databases linked together... but again, let's focus on a single server here with a singly MySQL DB.
If you use a single server, I don't think there is a problem with how big the table gets. It isn't just the number of records in a table, but how frequently it is accessed.
To manage large datasets, you can use multiple servers. In this case:
You can keep all workorders in a single table, and mirror them across different servers (so that you have slave servers)
You can shard the workorders table by boss (in this case you access the server depending on where the workorder belongs) - search for database sharding for more information
Which option you choose depends on how you will use your database.
Mirrors (master/slave)
Keeping all workorders in a single table is good for querying when you don't know which boss a workorder belongs to, eg. if you are searching by product type, but any boss can have orders in any product type.
However, you have to store a copy of everything on every mirror. In addition only one server (the master) can deal with update (or adding workorder) SQL requests. This is fine if most of your SQL queries are SELECT queries.
Sharding
The advantage of sharding is that you don't have to store a copy of the record on every mirror server.
However, if you are searching workorders by some attribute for any boss, you would have to query every server to check every shard.
How to choose
In summary, use a single table if you can have all sorts of queries, including browsing workorders by an attribute (other than which boss it belongs to), and you are likely to have more SELECT (read) queries than write queries.
Use shards if you can have write queries on the same order of magnitude as read queries, and/or you want to save memory, and queries searching by other attributes (not boss) are rare.
Keeping queries fast
Large databases are not really a big problem, if they are not overwhelmed by queries, because they can keep most of the database on hard disk, and only keep what was accessed recently in cache (on memory).
The other important thing to prevent any single query from running slowly is to make sure you add the right index for each query you might perform to avoid linear searches. This is to allow the database to binary search for the record(s) required.
If you need to maintain a count of records, whether of the whole table, or by attribute (category or boss), then keep counter caches.
When to get a new server
There isn't really a single number you can assign to determine when a new server is needed because there are too many variables. This decision can be made by looking at how fast queries are performing, and the CPU/memory usage of your server.
Scaling is often a case of experimentation as it's not always clear from the outset where the bottlenecks will be. Since you seem to have a pretty good idea of the kind of load the system will be under, one of the first things to do is capture this in a spreadsheet so you can work out some hypotheticals. This allows you do do a lot of quick "what if" scenarios and come up with a reasonable upper end for how far you have to scale with your first build.
For collecting large numbers of records there's some straight-forward rules:
Use the most efficient data type to represent what you're describing. Don't worry about using smaller integer types to shave off a few bytes, or shrinking varchars. What's important here is using integers for numbers, date fields for dates, and so on. Don't use a varchar for data that already has a proper type.
Don't over-index your table, add only what is strictly necessary. The larger the number of indexes you have, the slower your inserts will get as the table grows.
Purge data that's no longer necessary. Where practical delete it. Where it needs to be retained for an extended period of time, make alternate tables you can dump it into. For instance, you may be able to rotate out your main orders table every quarter or fiscal year to keep it running quickly. You can always adjust your queries to run against the other tables if required for reporting. Keep your working data set as small as practical.
Tune your MySQL server by benchmarking, tinkering, researching, and experimenting. There's no magic bullet here. There's many variables that may work for some people but might slow down your application. They're also highly dependent on OS, hardware, and the structure and size of your data. You can easily double or quadruple performance by allocating more memory to your database engine, for instance, either InnoDB or MyISAM.
Try using other MySQL forks if you think they might help significantly. There are a few that offer improved performance over the regular MySQL, Percona in particular.
If you query large tables often and aggressively, it may make sense to de-normalize some of your data to reduce the number of expensive joins that have to be done. For instance, on a message board you might include the user's name in every message even though that seems like a waste of data, but it makes displaying large lists of messages very, very fast.
With all that in mind, the best thing to do is design your schema, build your tables, and then exercise them. Simulate loading in 6-12 months of data and see how well it performs once really loaded down. You'll find all kinds of issues if you use EXPLAIN on your slower queries. It's even better to do this on a development system that's slower than your production database server so you won't have any surprises when you deploy.
The golden rule of scaling is only optimize what's actually a problem and avoid tuning things just because it seems like a good idea. It's very easy to over-engineer a solution that will later do the opposite of what you intend or prove to be extremely difficult to un-do.
MySQL can handle millions if not billions of rows without too much trouble if you're careful to experiment and prove it works in some capacity before rolling it out.
i had database size problem as well in one of my networks so big that it use to slow the server down when i run query on that table..
in my opinion divide your database into dates decide what table size would be too big for you - let say 1 million entries then calculate how long it will take you to get to that amount. and then have a script every that period of time to either create a new table with the date and move all current data over or just back that table up and empty it.
like putting out dated material in archives.
if you chose the first option you'll be able to access that date easily by referring to that table.
Hope that idea helps
Just create a workers table, bosses table, a relationships table for the two, and then all of your other tables. With a relationship structure like this, it's very dynamic. Because, if it ever got large enough you could create another relationship table between the work orders to the bosses or to the workers.
You might want to look into bigints, but I doubt you'll need that. I know it that the relationships table will get massive, but thats good db design.
Of course bigint is for mySQL, which can go up to -9223372036854775808 to 9223372036854775807 normal. 0 to 18446744073709551615 UNSIGNED*
I'm building an analytics system for a mobile application and have had some difficulty deciding how to store and process large amounts of data.
Each row will represent a 'view' (like a web page) and store some fixed attributes, like user agent and date. Additionally, each view may have a varying number of extra attributes, which relate to actions performed or content identifiers.
I've looked at Amazon SimpleDb which handles the varying number of attributes well, but has no support for GROUP BY and doesn't seem to perform well when COUNTing rows either. Generating a monthly graph with 30 data points would require a query for each day per dataset.
MySQL handles the COUNT and GROUP modifiers much better but additional attributes require storage in a link table and a JOIN to retrieve views where attributes match a given value, which isn't very fast. 5.1's partitioning feature may help speed things up a bit.
What I have gathered from a lot of reading and profiling queries on the aforementioned systems is that ultimately all of the data needs to be aggregated and stored in tables for quick report generation.
Have I missed anything obvious in my research and is there a better way to do this than use MySQL? It doesn't feel like the right task for the job, but I can't find anything capable of both GROUP/COUNT queries and a flexible table structure.
This is a case where you want to store the data once and read it over and over. Further I think that you'd wish the queries to be preprocessed instead of needing to be calculated on every go.
My suggestion for you is to store your data in CouchDB for the following reasons:
Its tables are structureless
Its queries are pre-processed
Its support for map-reduce allows your queries to handle group by
It has a REST service access model which lets you connect from pretty much anything that handle HTTP requests
You may find this suggestion a little out there considering how new CouchDB is. However I'd suggest for you to read about it because personally I think running a CouchDB database is sweet and lightweight. More light weight than MySQL
Keeping it in MySQL: If the amount of writes are limited / reads are more common, and the data is relatively simple (i.e: you can predict possible characters), you could try to use a text/blob column in the main table, which is updated with comma separated values or key/value pairs with an AFTER INSERT / UPDATE trigger on the join table. You keep the actual data in a separate table, so searching for MAX's / specific 'extra' attributes can still be done relatively fast, but retrieving the complete dataset for one of your 'views' would be a single row in the main table, which you can split into the separate values with the script / application you're using, relieving much of the stress on the database itself.
The downside of this is a tremendous increase in cost of updates / inserts in the join table: every alteration of data would require a query on all related data for a record, and a second insert into the 'normal' table, something like
UPDATE join_table
JOIN main_table
ON main_table.id = join_table.main_id
SET main_table.cache = GROUP_CONCAT(CONCAT(join_table.key,'=',join_table.value) SEPARATOR ';')
WHERE join_table.main_id = 'foo' GROUP BY main_table.id`).
However, as analytics data goes it usually trails somewhat, so possibly not every update has to trigger an update in cache, just a daily cronscript filling the cache with yesterdays data could do.