I have been using MySQL for years, mainly on smaller projects until the last year or so. I'm not sure if it's the nature of the language or my lack of real tutorials that gives me the feeling of being unsure if what I'm writing is the proper way for optimization purposes and scaling purposes.
While self-taught in PHP I'm very sure of myself and the code I write, easily can compare it to others and so on.
With MySQL, I'm not sure whether (and in what cases) an INNER JOIN or LEFT JOIN should be used, nor am I aware of the large amount of functionality that it has. While I've written code for databases that handled tens of millions of records, I don't know if it's optimum. I often find that a small tweak will make a query take less than 1/10 of the original time... but how do I know that my current query isn't also slow?
I would like to become completely confident in this field in the ability to optimize databases and be scalable. Use is not a problem -- I use it on a daily basis in a number of different ways.
So, the question is, what's the path? Reading a book? Website/tutorials? Recommendations?
EXPLAIN is your friend for one. If you learn to use this tool, you should be able to optimize your queries very effectively.
Scan the the MySQL manual and read Paul DuBois' MySQL book.
Use EXPLAIN SELECT, SHOW VARIABLES, SHOW STATUS and SHOW PROCESSLIST.
Learn how the query optimizer works.
Optimize your table formats.
Maintain your tables (myisamchk, CHECK TABLE, OPTIMIZE TABLE).
Use MySQL extensions to get things done faster.
Write a MySQL UDF function if you notice that you would need some
function in many places.
Don't use GRANT on table level or column level if you don't really need
it.
http://dev.mysql.com/tech-resources/presentations/presentation-oscon2000-20000719/index.html
The only way to become an expert in something is experience and that usually takes time. And a good mentor(s) that are better than you to teach you what you are missing. The problem is you don't know what you don't know.
Research and experience - if you don't have the projects to warrant the research, make them. Make three tables with related data and make up scenarios.
E.g.
Make a table of movies their data
make a table of user
make a table of ratings for users
spend time learning how joins work, how to get movies of a particular rating range in one query, how to search the movies table ( like, regex) - as mentioned, use explain to see how different things affect speed. Make a day of it; I guarantee your
handle on it will be greatly increased.
If you're still struggling for case-scenarios, start looking here on SO for questions and try out those scenarios yourself.
I don't know if MIT open courseware has anything about databases... Well whaddya know? They do: http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-830Fall-2005/CourseHome/
I would recommend that as one source based only on MITs reputation. If you can take a formal course from a university you may find that helpful. Also a good understanding of the fundamental discrete mathematics/logic certainly would do no harm.
As others have said, time and practice is the only real approach.
More practically, I found that EXPLAIN worked wonders for me personally. Learning to read the output of that was probably the biggest single leap I made in being able to write efficient queries.
The second thing I found really helpful was SQL Tuning by Dan Tow, which describes a fairly formal methodology for extracting performance. It's a bit involved, but works well in lots of situations. And if nothing else, it will give you a much better understanding of the way joins are processed.
Start with a class like this one: https://www.udemy.com/sql-mysql-databases/
Then use what you've learned to create and manage a number of SQL databases and run queries. Getting to the expert level is really about practice. But of course you need to learn the pieces before you can practice.
Related
I'm a new guy to databases, and I'm trying to figure out a good solution for dealing with large datasets. I mostly do statistical analyses using R, so I don't need a database as the backend of web pages or anything. By datasets are generally static - they are just big.
I was trying to do a simple left join of a ~10,000,000 record table on a ~1,400,000 table. The 1.4 m table had unique records. After churning for 3 hours, it quit on me. The query was specified correctly - I ran it limiting the retrievals to 1000 records and it returned exactly as I expected. Eventually, I found a way to split this up into 10 queries and it ran, but by this time, I was able to do that merge in R pretty quickly, without all the fancy calls to sqlite and indexing.
I've been looking to use databases because I thought they were faster/more effective for these basic data manipulations, but maybe I'm just overlooking something. In the above example, I had indexed in the appropriate columns, and I'm surprised that sqlite could not handle it whilst R could.
Sorry if this question is a little foggy (I'm a little foggy on databases), but if anyone has any advice on something obvious I'm doing wrong to not take advantage of the power of sqlite, that would be great. Or am I just expecting to much of of it, and a 100 m X 1.4 m record merge is just too big to execute without breaking it up?
I would think that a database could outperform R in this respect?
thanks!
EXL
I am going through the same process. If you look through the questions I've asked recently, you may get some good pointers, or at least avoid a lot of the time I've wasted :). In short, here's what's been most helpful to me.
-- the RSQLite package
-- the RSQLite.extfuns package
-- the SQLite FAQ
I'm still a newbie, but in general, you should be using SQLite for subsetting data that is too large to bring in to RAM. I would think that if the data are small enough to handle in RAM, then you're better off using the native R tools for joins/subsets. If you find that you become more comfortable with SQL queries, then there is the sqldf package. Also, JD Long has a great discussion on using sqldf with large datasets.
I have to admit that I'm surprised that this has been a problem for you. SQLite has always worked well for me, at least speed-wise. However -- SQLite is easy because it is so flexible. SQLite can be dangerous because it is so flexible. SQLite tends to be very forgiving with data types. Sometimes this is an absolute god-send, when I don't want to spend a bunch of time tweaking things to perfection, but with great flexibility comes great responsibility.
I have noticed that I need to be careful moving data into SQLite. Text is easy. However, sometimes numbers get stored as text rather than numbers. Doing a JOIN on a column of numbers is faster than the same JOIN on a column of text. If your number columns are stored as text and then coerced into numbers for the comparison, you would lose most of the advantage of using an index.
I don't know how you got your data into SQLite, so the first thing I would do is look at your table schemas and make sure they make sense. And while they may seem obvious, indexes can be tricky. Taking a look at the queries might also result in something useful.
Without being able to see the underlying structure and queries, answers to this question will be educated guesses.
I have some pretty complex reports to write. Some of them... I'm not sure how I could write an sql query for just one of the values, let alone stuff them in a single query.
Is it common to just pull a crap load of data and figure it all via code instead? Or should I try and find a way to make all the reports rely on sql?
I have a very rich domain model. In fact, parts of code can be expanded on to calculate exactly what they want. The actual logic is not all that difficult to write - and it's nicer to work my domain model than with SQL. With SQL, writing the business logic, refactoring it, testing it and putting it version control is a royal pain because it's separate from your actual code.
For example, one the statistics they want is the % of how much they improved, especially in relation to other people in the same class, the same school, and compared to other schools. This requires some pretty detailed analysis of how they performed in the past to their latest information, as well as doing a calculation for the groups you are comparing against as a whole. I can't even imagine what the sql query would even look like.
The thing is, this % improvement is not a column in the database - it involves a big calculation in of itself by analyzing all the live data in real-time. There is no way to cache this data in a column as doing this calculation for every row it's needed every time the student does something is CRAZY.
I'm a little afraid about pulling out hundreds upon hundreds of records to get these numbers though. I may have to pull out that many just to figure out 1 value for 1 user... and if they want a report for all the users on a single screen, it's going to basically take analyzing the entire database. And that's just 1 column of values of many columns that they want on the report!
Basically, the report they want is a massive performance hog no matter what method I choose to write it.
Anyway, I'd like to ask you what kind of solutions you've used to these kind of a problems.
Sometimes a report can be generated by a single query. Sometimes some procedural code has to be written. And sometimes, even though a single query CAN be used, it's much better/faster/clearer to write a bit of procedural code.
Case in point - another developer at work wrote a report that used a single query. That query was amazing - turned a table sideways, did some amazing summation stuff - and may well have piped the output through hyperspace - truly a work of art. I couldn't have even conceived of doing something like that and learned a lot just from readying through it. It's only problem was that it took 45 minutes to run and brought the system to its knees in the process. I loved that query...but in the end...I admit it - I killed it. ((sob!)) I dismembered it with a chainsaw while humming "Highway To Hell"! I...I wrote a little procedural code to cover my tracks and...nobody noticed. I'd like to say I was sorry, but...in the end the job ran in 30 seconds. Oh, sure, it's easy enough to say "But performance matters, y'know"...but...I loved that query... ((sniffle...)) Anybody seen my chainsaw..? >;->
The point of the above is "Make Things As Simple As You Can, But No Simpler". If you find yourself with a query that covers three pages (I loved that query, but...) maybe it's trying to tell you something. A much simpler query and some procedural code may take up about the same space, page-wise, but could possibly be much easier to understand and maintain.
Share and enjoy.
Sounds like a challenging task you have ahead of you. I don't know all the details, but I think I would go at it from several directions:
Prioritize: You should try to negotiate with the "customer" and prioritize functionality. Chances are not everything is equally useful for them.
Manage expectations: If they have unrealistic expectations then tell them so in a nice way.
IMHO SQL is good in many respects, but it's not a brilliant programming language. So I'd rather just do calculations in the application rather than in the database.
I think I'd go for some delay in the system .. perhaps by caching calculated results for some minutes before recalculating. This is with a mind towards performance.
The short answer: for analysing large quantities of data, a SQL database is probably the best tool around.
However, that does not mean you should analyse this straight off your production database. I suggest you look into Datawarehousing.
For a one-off report, I'll write the code to produce it in whatever I can best reason about it in.
For a report that'll be generated more than once, I'll check on who is going to be producing it the next time. I'll still write the code in whatever I can best reason about it in, but I might add something to make it more attractive to use to that other person.
People usually use a third party report writing system rather than writing SQL. As an application developer, if you're spending a lot of time writing complex reports, I would severely question your manager's actions in NOT buying an off-the-shelf solution and letting less-skilled people build their own reports using some GUI.
I have been working on sql server and front end coding and have usually faced problem formulating queries.
I do understand most of the concepts of sql that are needed in formulating queries but whenever some new functionality comes into the picture that can be dont using sql query, i do usually fails resolving them.
I am very comfortable with select queries using joins and all such things but when it comes to DML operation i usually fails
For every query that i never done before I usually finds uncomfortable with that while creating them. Whenever I goes for an interview I usually faces this problem.
Is it their some concept behind approaching on formulating sql queries.
Eg.
I need to create an sql query such that
A table contain single column having duplicate record. I need to remove duplicate records.
I know i can find the solution to this query very easily on Googling, but I want to know how everyone comes to the desired result.
Is it something like Practice Makes Man Perfect i.e. once you did it, next time you will be able to formulate or their is some logic or concept behind.
I could have get my answer of solving above problem simply by posting it on stackoverflow and i would have been with an answer within 5 to 10 minutes but I want to know the reason. How do you work on any new kind of query. Is it a major contribution of experience or some an implementation of concepts.
Whenever I learns some new thing in coding section I tries to utilize it wherever I can use it. But here scenario seems to be changed because might be i am lagging in some concepts.
EDIT
How could I test my knowledge and
concepts in Sql and related sql
queries ?
Typically, the first time you need to open a child proof bottle of pills, you have a hard time, but after that you are prepared for what it might/will entail.
So it is with programming (me thinks).
You find problems, research best practices, and beat your head against a couple of rocks, but in the process you will come to have a handy set of tools.
Also, reading what others tried/did, is a good way to avoid major obsticles.
All in all, with a lot of practice/coding, you will see patterns quicker, and learn to notice where to make use of what tool.
I have a somewhat methodical method of constructing queries in general, and it is something I use elsewhere with any problem solving I need to do.
The first step is ALWAYS listing out any bits of information I have in a request. Information is essentially anything that tells me something about something.
A table contain single column having
duplicate record. I need to remove
duplicate
I have a table (I'll call it table1)
I have a
column on table table1 (I'll call it col1)
I have
duplicates in col1 on table table1
I need to remove
duplicates.
The next step of my query construction is identifying the action I'll take from the information I have.
I'll look for certain keywords (e.g. remove, create, edit, show, etc...) along with the standard insert, update, delete to determine the action.
In the example this would be DELETE because of remove.
The next step is isolation.
Asnwer the question "the action determined above should only be valid for ______..?" This part is almost always the most difficult part of constructing any query because it's usually abstract.
In the above example you're listing "duplicate records" as a piece of information, but that's really an abstract concept of something (anything where a specific value is not unique in usage).
Isolation is also where I test my action using a SELECT statement.
Every new query I run gets thrown through a select first!
The next step is execution, or essentially the "how do I get this done" part of a request.
A lot of times you'll figure the how out during the isolation step, but in some instances (yours included) how you isolate something, and how you fix it is not the same thing.
Showing duplicated values is different than removing a specific duplicate.
The last step is implementation. This is just where I take everything and make the query...
Summing it all up... for me to construct a query I'll pick out all information that I have in the request. Using the information I'll figure out what I need to do (the action), and what I need to do it on (isolation). Once I know what I need to do with what I figure out the execution.
Every single time I'm starting a new "query" I'll run it through these general steps to get an idea for what I'm going to do at an abstract level.
For specific implementations of an actual request you'll have to have some knowledge (or access to google) to go further than this.
Kris
I think in the same way I cook dinner. I have some ingredients (tables, columns etc.), some cooking methods (SELECT, UPDATE, INSERT, GROUP BY etc.) then I put them together in the way I know how.
Sometimes I will do something weird and find it tastes horrible, or that it is amazing.
Occasionally I will pick up new recipes from the internet or friends, then use parts of these in my own.
I also save my recipes in handy repositories, broken down into reusable chunks.
On the "Delete a duplicate" example, I'd come to the result by googling it. This scenario is so rare if the DB is designed properly that I wouldn't bother keeping this information in my head. Why bother, when there is a good resource is available for me to look it up when I need it?
For other queries, it really is practice makes perfect.
Over time, you get to remember frequently used patterns just because they ARE frequently used. Rare cases should be kept in a reference material. I've simply got too much other stuff to remember.
Find a good documentation to your software. I am using Mysql a lot and Mysql has excellent documentation site with decent search function so you get many answers just by reading docs. If you do NOT get your answer at least you are learning something.
Than I set up an example database (or use the one I am working on) and gradually build my SQL. I tend to separate the problem into small pieces and solve it step by step - this is very successful if you are building queries including many JOINS - it is best to start with some particular case and "polute" your SQL with many conditions like WHEN id = "123" which you are taking out as you are working towards your solution.
The best and fastest way to learn good SQL is to work with someone else, preferably someone who knows more than you, but it is not necessarry condition. It can be replaced by studying mature code written by others.
Your example is a test of how well you understand the DISTINCT keyword and the GROUP BY clause, which are SQL's ways of dealing with duplicate data.
Examples and experience. You look at other peoples examples and you create your own code and once it groks, you don't need to think about it again.
I would have a look at the Mere Mortals book - I think it's the one by Hernandez. I remember that when I first started seriously with SQL Server 6.5, moving from manual ISAM databases and Access database systems using VB4, that it was difficult to understand the syntax, the joins and the declarative style. And the SQL queries, while powerful, were very intimidating to understand - because typically, I was looking at generated code in Microsoft Access.
However, once I had developed a relatively systematic approach to building queries in a consistent and straightforward fashion, my skills and confidence quickly moved forward.
From seeing your responses you have two options.
Have a copy of the specification for whatever your working on (SQL spec and the documentation for the SQL implementation (SQLite, SQL Server etc..)
Use Google, SO, Books, etc.. as a resource to find answers.
You can't formulate an answer to a problem without doing one of the above. The first option is to become well versed into the capabilities of whatever you are working on.
The second option allows you to find answers that you may not even fully know how to ask. You example is fairly simplistic, so if you read the spec/implementation documentaion you would know the answer right away. But there are times, where even if you read the spec/documentation you don't know the answer. You only know that it IS possible, just not how to do it.
Remember that as far as jobs and supervisors go, being able to resolve a problem is important, but the faster you can do it the better which can often be done with option 2.
I don't have too much experience with SQL. Most of the queries I have written have been very small. Whenever I see a very large query, I always kinda assume it needs to be optimized. But is this true? or is there situations where really large queries are just whats needed?
BTW when I say large queries I mean queries that exceed 1000+ chars
Yes, any statement, method, or even query can be "too big".
The problem, is actually defining what too big really is.
If you can't sit down and figure out what the query does in a relatively short amount of time, it's probably best to break it up into smaller chunks.
I always like to look at things from a maintenance standpoint. If the query is hard to understand now, what if you have to debug something in it?
Just because you see a large query, doesn't mean it needs to be changed or optimized, but if it's too complicated for its own good, then you might want to consider refactoring.
Just as in other languages, you can't determine the efficiency of a query based on a character count. Also, 1000 characters isn't what I could call "large", especially when you use good table/column names, aliases that make sense, etc.
If you're not comfortable enough with SQL to be able to "eye ball" the design merits of particular query, run it through a profiler and examine the execution plan. That'll give you a good idea of problems, if any, the code in question will suffer from.
My rule of thumb is this: write the best, tightest, simplest code you can, and optimize where needed - ie, where you see a performance bottleneck or where (as frequently happens) you slap yourself in the head and say "D'OH!" at about three in the morning on vacation.
Summary:Code well, and optimize where needed.
As Robert said, if you can't easily tell what the query is doing, it probably needs to be simplified.
If you are used to writing simple stuff, you may not realize how complex getting information for a complex report might be. Yes, queries can get long and complicated and still perform well for what they are being asked to do. Often the techniques that are used to performance tune something may make the code look more complicated to those less familar with advanced querying techniques. What counts is how long it takes to execute and whether it returns the correct data, not how many characters it has.
When I see a complex query, my first thought is does it return what the developer really intended to return (you'd be surprised at how often the answer to that is no) and then I look to see if it could be performance tuned. Yes there are many badly written long queries out there, but there are also as many or more that do what they are intended to do about as fast as it can be done without a major database redesign or faster hardware.
I'd suggest that it's not the characters that should measure the size/complexity of the query.
I'd boil it down to:
what's the goal of the query?
does it used set-based logic?
does it re-use any components?
does it JOIN improperly/poorly?
what are the performance implications?
maintainability concerns - is it written so that another developer can grok its intentions?
Where I work we've created stored procedures that exceed 1000 characters. I can't really say it was NECESSARY but sometimes haste wins out over efficiency (most notably when a quick fix is necessary for a client).
Having said that ... if given the time I would attempt to optimize a query as small/efficient as it can get without it being overly confusing. I've used nested stored procedures to make things a little more clear and/or functions as well.
The number of characters does not mean that a query needs to be optimized - it is what you see within those characters that does.
Things like subqueries on top of subqueries is something I would review. I'd review JOINs as well, but it shouldn't take long comparing to the ERD to know if there's an unnecessary JOIN - the first thing I'd look at would be what tables are joined but not used in the output, which is fine if the tables are link/corrollary/etc tables.
How do I stop thinking every query in terms of cursors, procedures and functions and start using SQL as it should be? Do we make the transition to thinking in SQL just by practise or is there any magic to learning the set based query language? What did you do to make the transition?
A few examples of what should come to your mind first if you're real SQL geek:
Bible concordance is a FULLTEXT index to the Bible
Luca Pacioli's Summa de arithmetica which describes double-entry bookkeeping is in fact a normalized database schema
When Xerxes I counted his army by walling an area that 10,000 of his men occupied and then marching the other men through this enclosure, he used HASH AGGREGATE method.
The House That Jack Built should be rewritten using a self-join.
The Twelve Days of Christmas should be rewritten using a self-join and a ROWNUM
There Was An Old Woman Who Swallowed a Fly should be rewritten using CTE's
If the European Union were called European Union All, we would see 27 spellings for the word euro on a Euro banknote, instead of 2.
And finally you can read a lame article in my blog on how I stopped worrying and learned to love SQL (I almost forgot I wrote it):
Click
And one more article just on the subject:
Double-thinking in SQL
The key thing is you're manipulating SETS & elements of sets; and relating different sets (and corresponding elements) together. That's really the heart of it, imho. That's why every table should have a primary key; why you see set operators in the language; and why set operators like UNION won't (by defualt) return duplicate rows.
Of course in practice, the rules of sets are bent or broken but it's not that hard to see when this is necessary (otherwise, SQL would be TOO limited). Imho, just crack open your discrete math book and reacquaint yourself with some set exercises.
Best advice I can give you is that every time you think about processing something row-by-row, that you stop and ask yourself if there is a set-based way to do this.
Joe Celko's Thinking in Sets (book)
Perfectly intelligent programmers
often struggle when forced to work
with SQL. Why? Joe Celko believes the
problem lies with their procedural
programming mindset, which keeps them
from taking full advantage of the
power of declarative languages. The
result is overly complex and
inefficient code, not to mention lost
productivity.
This book will change the way you
think about the problems you solve
with SQL programs.. Focusing on three
key table-based techniques, Celko
reveals their power through detailed
examples and clear explanations. As
you master these techniques, you’ll
find you are able to conceptualize
problems as rooted in sets and
solvable through declarative
programming. Before long, you’ll be
coding more quickly, writing more
efficient code, and applying the full
power of SQL.
When people ask me about joins I send them here it has a great visual representation on what they are!
The way that I learned was by doing a lot of queries, and working at a job that required you to think in terms of result sets.
From your question, it seems like you've been writing lots of front-end code that uses sequential/procedural/iterative data manipulation. If you don't get on any projects that require you to use result set skills, I personally wouldn't worry about it.
One thing you might want to try is by trying to write analytical queries, e.g., generating simplistic reports on your data. In those cases you are trying to summarize large amounts of data by cordoning them off into sets.
Another good way would be to read a book on the theoretical/mathematical foundations to RDBMSes. Those deal strictly with set theory and how parts of the SQL query syntax relate directly with the math behind it. Of course, this requires you to like math. :)
I found that the Art Of SQL was a useful kick in the head for getting into the right mindset.
Part of this, however, comes down to style.
Obviously, you need to start thinking in result sets and not just procedurally.
However, once you've start that, you will often find decisions have to be made.
Do you write the incredibly complex update statement that may be difficult to understand by anyone but yourself, and difficult to maintain, or do you write a less efficient, but easier to manage procedure?
I would HIGHLY suggest that you remember that SQL statements can have comments in them to clarifiy what they are doing, not just stored procedures.
link: The Art Of SQL
One exercise you might want to try is this:
Take some of your existing reporting code from your application layer, preferably something that produces a single, tabular data set. Starting with the most basic elements, port it over to an SQL View.
Take all of the columns pulled from a single table and write the SQL statement to select that data. Then join on one table at a time and start figuring out the appropriate conditions and logic for your output.
You might come up against some particular task that at first seems impossible in SQL, but depending on the implementation you are programming against, there is almost always a way to get the result you're looking for. Check the documentation for your SQL implementation, or try Google.
This exercise has the benefit of giving you an original report to test against, so you know if you're getting the output you expect.
A few things to watch out for:
Recursion and graphs are fairly advanced techniques; you might want to start with something easier. (Joe Celko has a good book on the topic, if you're interested.)
There's often a big difference between a BIT and a C-style bool. At the very least, you may have to explicitly cast your output from INT to BIT.
OUTER JOINs are useful when a portion of the data might be empty, but try not to abuse them.
I think it takes a while to adjust (it was long ago for me, so I don't remember too well). But perhaps the key point is that SQL is declarative - i.e. you specify what you want done, not precisely how it should be done procedurally. So for a simple example:
"Get me the names and salaries of employees in departments located in London"
The relevant SQL is almost natural:
select name, salary
from employees
join departments on departments.deptno = employees.deptno
where departments.location = 'London';
We have "told" SQL how to join departments to employees, but only declaratively (NATURAL JOIN removes the need to do that, but is dangerous so not used in practice). We haven't defined procedurally how it should be done (e.g. "for each department, find all employees...") SQL is free to choose the optimal method to perform the query.
Thinking of rows makes sense when you use SQL to dump a table to your file system and then do whatever has to be done in your favorite programming language.
Not too much leverage of SQL; waste of disk, memory, cpu and human resources.
Think of SQL as of English (or whatever human language you prefer).
Show me all customers who ride bulls and get drunk every day but never visited Indonesia with their mother-in-law whose phone number is the same as my friend Doug's except for the area code.
You can do it (and much more) in one SQL statement, just learn how to. It's very lucrative.