Nearly all database implementations offer the possibility of creating indexes, based on various data structures, that greatly speed up search speed.
Is it possible for any database - especially the most-used ones, such as MySQL, Postgres, MongoDB, etc - offer the ability to see how records are being stored? As in, to actually see the B-tree?
In Postgres you can use the pageinspect extension. It provides functions that allow you to inspect the contents of database pages at a low level.
MySQL doesn't have any official tools to view internal index data structures, but developer Jeremy Cole created a set of tools to do that. He wrote about what he discovered in a series of blog posts:
https://blog.jcole.us/innodb/
He demonstrates his InnoDB inspection tools, which he made freely available on Github.
Related
I want to use aws for hosting a django application and use aws rds for database purpose. The application is kind of blog like system.
I am not able to decide which RDS I should choose over MySql or Postgres? Both price wise and performance wise according to aws pricing policy.
This can be very broad and may be opinionated , I would try to keep it short as i read it somewhere:
MySQL would be very good for any CMS Site as it works very well with it and MyISAM tables are quite nice here.
From What I read where PostgreSQL does better than MySQL:
Multi-application databases
Advanced data modelling
What Advance Data Modelling means is that PostgreSQL is far more mature at doing complex data modelling than MySQL is. It has a very mature extensible type system, a wide range of procedural languages, and a great deal of flexibility in how these languages can be plugged into existing queries.
If that wasn't enough, the fact is that you can essentially build your data model in PostgreSQL based not only on what information you are storing but what information is commonly derived from what you are storing. This makes things like not-first-normal-form actually sane to use where they are needed. Add collections and multiple inheritance in table structure and you have a very sophisticated data modelling platform, this blog would help you understand it better.
Besides the content management system market, MySQL's other major market is in applications where data is not expected to be exposed to more than one writing application at a time. This leads to a significant difference in handling data validation, etc.
In PostgreSQL validation is always equally strict. If the app expects special error treatment it had better call functions or casts to handle this explicitly.
MySQL however places the application in charge of defining the data validation rules.So while PostgreSQL allows the relational and object-relational interface to serve as a public API, it is essentially intended largely to be a private API for applications in MySQL. This is a huge difference and not readily understood by many people trying to make the choice. This leads to major differences in application design.
MySQL is a data storage and reporting solution for your application.
PostgreSQL is a data centralization, modelling, and reporting solution
for your organization. The two are remarkably different.
Now coming to Second Question based on pricing as you can see from MySQL Pricing Page and PostgreSQL Pricing Page MySQL is bit cheaper than PostgrSQL , reading on the answer you can make informed decision what would be best for you.
Hope this Helps!
I'm gonna offer you a 3rd option: Aurora - try it. It's cheaper than those 2 and is MySQL compatible.
This article may be of help to you when deciding.
For simple blog-like thingie I'd go with MySQL (or Aurora MySQL compatible version)
For data-critical and highly relational solutions I might also consider Postgres (Aurora)
I am working on a web application that is based on a MySQL database. I need to collect and analyse usage and performance statistics. The statistics will be aimed at non-technical personnel.
How can I implement this feature? You should treat my question as a programming question but in case you know of a tool or extension that would be suitable please mention it.
The official MySQL client, MySQL Workbench includes the feature to visualize Performance Schema since version 6.1. It's in Performance section in the software.
Read more at: http://mysqlworkbench.org/
I'm planning out a Rails app that will be hosted on Heroku and will need both geospatial and full text search capabilities.
I know that Heroku offers add-ons like WebSolr and IndexTank that sound like they can do the job, but I was wondering if this could be done in MySQL and/or PostgreSQL without having to pay for any add-ons?
Depending on the scale of your application you should be able to accomplish both FULLTEXT and SPATIAL indexes in MySQL with ease. Once your application gets massive, i.e hundreds of millions of rows with high concurrency and multiples of thousands of requests per second you might need to move to another solution for either FULLTEXT or SPATIAL queries. But, I wouldn't recommend optimize for that early on, since it can be very hard to do properly. For the foreseeable future MySQL should suffice.
You can read about spatial indexes in MySQL here. You can read about fulltext indexes in MySQL here. Finally, I would recommend taking the steps outlined here to make your schema.rb file and rake tasks work with these two index types.
I have only used MySQL for both, but my understanding is that PostgreSQL has a good geo-spatial index solution as well.
If you have a database at Heroku, you can use Postgres's support for Full Text Search: http://www.postgresql.org/docs/8.3/static/textsearch.html. The oldest servers Heroku runs (for shared databases) are on 8.3 and 8.4. The newest are on 9.0.
A blog post noticing this little fact can be seen here: https://tenderlovemaking.com/2009/10/17/full-text-search-on-heroku.html
Apparently, that "texticle" (heh. cute.) addon works...pretty well. It will even create the right indexes for you, as I understand it.
Here's the underlying story: postgres full-text-search is pretty fast and fuss-free (although Rails-integration may not be great), although it does not offer the bells and whistles of Solr or IndexTank. Make sure you read about how to properly set up GIN and/or GiST indexes, and use the tsvector/tsquery types.
The short version:
Create an (in this case, expression-based) index: CREATE INDEX pgweb_idx ON pgweb USING gin(to_tsvector('english', body));. In this case "body" is the field being indexed.
Use the ## operator: SELECT * FROM ... WHERE to_tsvector('english', pgweb.body) ## to_tsquery('hello & world') LIMIT 30
The hard part may be mapping things back into application land, the blog post previously cited is trying to do that.
The dedicated databases can also be requisitioned with PostGIS, which is a very powerful and fully featured system for indexing and querying geographical data. OpenStreetMap uses the PostgreSQL geometry types (built-in) extensively, and many people combine that with PostGIS to great effect.
Both of these (full text search, PostGIS) take advantage of the extensible data type and indexing infrastructure in Postgres, so you should expect them to work with high performance for many, many records (spend a little time carefully reviewing the situation if things look busted). You might also take advantage of fact that you are able to leverage these features in combination with transactions and structured data. For example:
CREATE TABLE products (pk bigserial, price numeric, quantity integer, description text); can just as easily be used with full text search...any text field will do, and it can be in connection with regular attributes (price, quantity in this case).
I'd use thinking sphinx, a full text search engine also deployable on heroku.
It has geo search built-in: http://freelancing-god.github.com/ts/en/geosearching.html
EDIT:
Sphynx is almost ready for heroku, see here: http://flying-sphinx.com/
IndexTank is now free up to 100k documents on Heroku, we just haven't updated the documentation. This may not be enough for your needs, but I thought I'd let you know just in case.
For full text search via Postgre I recommend pg_search, I am using it myself on heroku at the moment. I have not used texticle but from what I can see pg_search has more development activity lately and it has been built upon texticle (it will not add indexes for you, you have to do it yourself).
I cannot find the thread now but I saw that Heroku gave option for pg geo search but it was in beta.
My advice is if you are not able to find postgre solution is to host your own instance of SOLR (on EC2 instance) and use sunspot solr gem to integrate it with rails.
I have implemented my own solution and used WebSolr as well. Basically that is what they give you their own SOLR instance hassle free. Is it worth the money, in my opinion no. For integration that use sunspot solr client as well, so it is just are you going to pay somebody 20$/40$/... to host SOLR for you. I know you also get backups, maintenance etc. but call me cheap I prefer my own instance. Also WebSolr is locked on 1.4.x version of SOLR.
Currently have approximately 2000 simultaneouse connections. We average approximately 425 reads and writes per second. We have a read to write ration of 3:1. All of our tables are myisam. Can we expect better or worse performance when we go from mysql 4.1.22 to 5.0?
There's no way for anyone here to tell you without the schema, queries and test data.
Why not setup a dev environment on 5.0 and testing it out?
The main concern should be that the 5.0 Information Schemas, are a HUGE vulnerability and can be used to very easily gain access to the SQL server from remote locations simply by printing off the schema using injection will let an unwanted viewer, view all of the tables and capitalize off the knowledge to get passwords using the same schema for its columns.
The MySQL source tree includes a set of benchmark tests written as Perl scripts. See The MySQL Benchmark Suite for some information. You can download the source distribution for MySQL 5.0.91 at the archives.
Source distribution of MySQL 4.1 doesn't seem to be easily available anymore. You might have to check it old sources from LaunchPad unless you can find a copy of an old source distribution elsewhere on the internet.
However, the comparison that these benchmarks show is only of general interest. It may be irrelevant to how your application performs. For instance, your usage of the database may not take advantage of some performance improvements in MySQL 5.0, but it may run into some regressions in MySQL 5.0 that were necessary.
The only way to get an answer that is relevant to your application is to try the new software with a test instance of your application, using a sample of data that is a realistic model of the type and volume of data your application typically deals with. As #BenS says, no one on a site like StackOverflow can give an answer specific to your application.
You say in a comment that you're very concerned about performance, but if you don't have an instance of your application and database that you can run tests on, you aren't doing the work necessary to satisfy this concern.
I would strongly suggest moving straight to 5.1.45 with Innodb Support. Percona provides an excellent version with XtraDB that provides a number of performance related improvements. Moving off of your MyISAM tables and onto Innodb will provide a huge performance increase in almost all cases. If you are going to burn the QA/Testing time to move, do a full move now, not a half-way step.
My Django project is going to be backed by a large database with several hundred thousand entries, and will need to support searching (I'll probably end up using djangosearch or a similar project.)
Which database backend is best suited to my project and why? Can you recommend any good resources for further reading?
For whatever it's worth the the creators of Django recommend PostgreSQL.
If you're not tied to any legacy
system and have the freedom to choose
a database back-end, we recommend
PostgreSQL, which achives a fine
balance between cost, features, speed
and stability. (The Definitive Guide to Django, p. 15)
As someone who recently switched a project from MySQL to Postgresql I don't regret the switch.
The main difference, from a Django point of view, is more rigorous constraint checking in Postgresql, which is a good thing, and also it's a bit more tedious to do manual schema changes (aka migrations).
There are probably 6 or so Django database migration applications out there and at least one doesn't support Postgresql. I don't consider this a disadvantage though because you can use one of the others or do them manually (which is what I prefer atm).
Full text search might be better supported for MySQL. MySQL has built-in full text search supported from within Django but it's pretty useless (no word stemming, phrase searching, etc.). I've used django-sphinx as a better option for full text searching in MySQL.
Full text searching is built-in with Postgresql 8.3 (earlier versions need TSearch module). Here's a good instructional blog post: Full-text searching in Django with PostgreSQL and tsearch2
large database with several hundred
thousand entries,
This is not large database, it's very small one.
I'd choose PostgreSQL, because it has a lot more features. Most significant it this case: in PostgreSQL you can use Python as procedural language.
Go with whichever you're more familiar with. MySQL vs PostgreSQL is an endless war. Both of them are excellent database engines and both are being used by major sites. It really doesn't matter in practice.
All the answers bring interesting information to the table, but some are a little outdated, so here's my grain of salt.
As of 1.7, migrations are now an integral feature of Django. So they documented the main differences that Django developers might want to know beforehand.
Backend Support
Migrations are supported on all backends that Django ships with, as
well as any third-party backends if they have programmed in support
for schema alteration (done via the SchemaEditor class).
However, some databases are more capable than others when it comes to schema migrations; some of the caveats are covered below.
PostgreSQL
PostgreSQL is the most capable of all the databases here in terms of schema support.
MySQL
MySQL lacks support for transactions around schema alteration operations, meaning that if a migration fails to apply you will have to manually unpick the changes in order to try again (it’s impossible to roll back to an earlier point).
In addition, MySQL will fully rewrite tables for almost every schema operation and generally takes a time proportional to the number of rows in the table to add or remove columns. On slower hardware this can be worse than a minute per million rows - adding a few columns to a table with just a few million rows could lock your site up for over ten minutes.
Finally, MySQL has relatively small limits on name lengths for columns, tables and indexes, as well as a limit on the combined size of all columns an index covers. This means that indexes that are possible on other backends will fail to be created under MySQL.
SQLite
SQLite has very little built-in schema alteration support, and so
Django attempts to emulate it by:
Creating a new table with the new schema
Copying the data across
Dropping the old table
Renaming the new table to match the original name
This process generally works well, but it can be slow and occasionally
buggy. It is not recommended that you run and migrate SQLite in a
production environment unless you are very aware of the risks and its
limitations; the support Django ships with is designed to allow
developers to use SQLite on their local machines to develop less
complex Django projects without the need for a full database.
Even if Postgresql looks better, I find it has some performances issues with Django:
Postgresql is made to handle "long connections" (connection pooling, persistant connections, etc.)
MySQL is made to handle "short connections" (connect, do your queries, disconnect, has some performances issues with a lot of open connections)
The problem is that Django does not support connection pooling or persistant connection, it has to connect/disconnect to the database at each view call.
It will works with Postgresql, but connecting to a Postgresql cost a LOT more than connecting to a MySQL database (On Postgresql, each connection has it own process, it's a lot slower than just popping a new thread in MySQL).
Then you get some features like the Query Cache that can be really useful on some cases. (But you lost the superb text search of PostgreSQL)
When a migration fails in django-south, the developers encourage you not to use MySQL:
! The South developers regret this has happened, and would
! like to gently persuade you to consider a slightly
! easier-to-deal-with DBMS (one that supports DDL transactions)
Having gone down the road of MySQL because I was familiar with it (and struggling to find a proper installer and a quick test of the slow web "workbench" interface of postgreSQL put me off), at the end of the project, after a few months after deployment, while looking into back up options, I see you have to pay for MySQL's enterprise back up features. Gotcha right at the very end.
With MySql I had to write some ugly monster raw SQL queries in Django because no select distinct per group for retrieving the latest per group query. Also looking at postgreSQL's full-text search and wishing I had used postgresSQL.
I recommend PostgreSQL even if you are familiar with MySQL, but your mileage may vary.
UPDATE: DBeaver is a great equivalent of MySql Workbench gui tool but works with PostgreSQL very nicely (and many others as its a universal DB tool).
To add to previous answers :
"Full text search might be better supported for MySQL"
The FULLTEXT index in MySQL is a joke.
It only works with MyISAM tables, so you lose ACID, Transactions, Constraints, Relations, Durability, Concurrency, etc.
INSERT/UPDATE/DELETE to a largish TEXT column (like a forum post) will a rebuild a large part of the index. If it does not fit in myisam_key_buffer, then large IO will occur. I've seen a single forum post insertion trigger 100MB or more of IO ... meanwhile the posts table is exclusiely locked !
I did some benchmarking (3 years ago, may be stale...) which showed that on large datasets, basically postgres fulltext is 10-100x faster than mysql, and Xapian 10-100x faster than postgres (but not integrated).
Other reasons not mentioned are the extremely smart query optimizer, large choice of join types (merge, hash, etc), hash aggregation, gist indexes on arrays, spatial search, etc which can result in extremely fast plans on very complicated queries.
Will this application be hosted on your own servers or by a hosting company? Make sure that if you are using a hosting company, they support the database of choice.
There is a major licensing difference between the two db that will affect you if you ever intend to distribute code using the db. MySQL's client libraries are GPL and PostegreSQL's is under a BSD like license which might be easier to work with.