My application needs configurable columns , and titles of these columns get configured in the begining, If relation database I would have created generic columns in table like CodeA, CodeB etc for this need because it helps queering on these columns (Code A = 11 ) it also helps in displaying the values (if that columns stores code and value) but now I am using Non Relational database Datastore (and I am new to it), should I follow the same old approach or I should use collection (Key Value pair) type of structure .
There will be lot of filters on these columns. Please suggest
What you've just described is one of the classic scenarios for a Key-Value database. The limitation here is that you will not have many of the set-based tools you're used to.
Most of the K-V databases are really good at loading one "record" or small set thereof. However, they don't tend to be any good at loading anything that may require a join. Given that you're using AppEngine, you probably appreciate this limitation. But it's worth stating.
As an important note, not all K-V database will allow you to "select by any column". Many K-V stores actually only allow for selection by a primary key. If you take a look at MongoDB, you'll find that you can query any column which sounds like a necessary feature.
I would suggest using key/value pairs where keys will act as your column names and value will be their data.
Related
In my PostgreSQL I have two tables board and cards tables with OneToMany relationship between them(one board can have a multiple cards).
User can hold a few cards on the board. In order to implement this functionality typically I would created another table called for example cards_on_hold with OneToMany relationship and placed cards on hold IDs into this table. In order to fetch this data for board I'd use JOIN between board and cards_on_hold.
Is there any more effective way in PostgreSQL to store cards on hold IDs ? Maybe for example some feature to store this list inline in board table ? I'll need to use this IDs list later in IN SQL clause in order to filter card set.
Postgres does support arrays of integers (assuming your ids are integers):
http://www.postgresql.org/docs/9.1/static/arrays.html
However manipulating that data is a bit hard compared to a separate table. For example with a separate table you can put a uniqueness guarantee so that you won't have duplicates of ids (assuming you'd want that). To achieve the same thing with an array you would have to create a stored procedure to detect duplicates (on insert for example). That would be hard (if possible at all) to be as efficient as simple unique constraint. Not to mention that you lose consistency guarantee because you can't put foreign key constraint on such array.
So in general conisistency would be an issue with inline list. At the same time I doubt you would get any noticable performance gain. After all arrays should not be used as an "aggregated foreign key" IMHO.
All in all: I suggest you stick to a separate table.
I want to store a list of users in a Cassandra Column Family(Wide rows).
The columns in the CF will have Composite Keys of pattern id:updated_time:name:score
After inserting all the users, i need to query users in a different sorted order each time.
For example, if i specify updated_time, i could be able to fetch the recent 10 users.
And, if i specify score, then i could be able to fetch the top 10 users based on score.
Does Cassandra supports this?
Kindly help me in this regard...
i need to query users in a different sorted order each time...
Does Cassandra supports this
It does not. Unlike a RDBMS, you can not make arbitrary queries and expect reasonable performance. Instead you must design you data model so the queries you anticipate will be made will be efficient:
The best way to approach data modeling for Cassandra is to start with your queries and work backwards from there. Think about the actions your application needs to perform, how you want to access the data, and then design column families to support those access patterns.
So rather than having one column family (table) for your data, you might want several with cross references between them. That is, you might have to denormalise your data.
We want to develop a application which need to support custom attribtues to different entities (like user, project, folder, document etc..) in our application.
I googled and prima face it looks like No-SQL database can be suited for our requirement. Do you see any limitation ? What are the prons/cons of using No-SQL instead of RDBMS?
There are many NO-SQL databases available - http://nosql-database.org/ ? But we don't have any experiance in using No SQL database.Don't find any good article which compares these NO-SQL database. Any suggestion which No-SQL data store we can use to achive custom attribtues functionality?
One big advantage of No-sql database is its free-style: you will never specify the columns like "user, project, folder" before you insert your real data. The columns can be added at any time.
While in RDBMS, the table schema is strictly defined, can not modify during run time.
Another advantage is the performance in query. It is quite efficient if you query all the records of a user, say "Michael", since the data is stored following the principle of Big Table, named by google.
There are two ways to solve your question: a column database such as Cassandra; or a name-value pair (also called attribute-value pair) in relational.
First, Cassandra is a structured key-value store. A key can contain multiple and variable attributes and values. Values or columns are grouped into column families. The column families are fixed when a Cassandra database is created. A family is analogous to an entity in a logical data model or to a table in relational. Columns can be added to a family at any time. Thereby, different instances of the column family can have different columns, which is what you need. Furthermore, columns are assigned to specified keys, so different keys can have different numbers of columns in any given family.
A name value pair, also called an attribute value pair, can be created in logical data modeling and in relational. This can be done with three related entities or tables:
The base entity (such as customer), which in analogous to a column family.
A "type" entity, which describes the attribute and its characteristics such as Net Worth Amount,
A "value" entity, which assigns the attribute to an instance of a base entity and assigns it a value.
The "type" entity is simply a code table identified by a type code and containing a description and other domain characteristics. Domain refers to data type, length, meaning, and units of measure. It describes the attribute out of context (i.e., unassigned). An example could be Net Worth Amount, which is a number 8 digits with 2 decimal places, right justified, and its description is "a value representing the total financial value of a customer including liquid and non-liquid amounts".
The "value" entity is an associative entity or table that is identified by the customer id and the attribute type code, and has a value attribute that assigns the Net Worth Amount type the Customer and gives it a value, such as "$2,000,000."
However, in relational name-value pairs are somewhat difficult to query in SQL and generally do not perform well. This could be addressed by denormalizing the "type" and "value" entities into one. Instead of having three tables you have two -- one-to-many. Actually, that is essentially how Cassandra does it. A column family is a fully flattened attribute-value pair.
I hope this helps. If you are going to use NOSQL, I'd use something like Cassandra. If you use relational, I'd denormalize (i.e., collapse into one) the type and value. The advantage of relational is that your already have it. The disadvantage to Cassandra is that you have to learn it but it is build to do what you want.
Couchbase would be a great answer for you, if you can encapsulate your model into JSON then you are already halfway there. You can have any number of properties for your object:
product::001
{
"name": "Hard Drive",
"brand": "Toshiba",
...
...
}
To learn some simple patterns moving from RDBMS to Couchbase, check out their webinars at http://www.couchbase.com/webinars or some simple design patterns at http://CouchbaseModels.com (examples are in Ruby though)
The real advantage of Couchbase is schema flexibility, horizontal scalability on commodity hardware, and speed. After learning the basics, it fits better into Agile processes, with almost no need for migrations. In enterprise organizations it's very effective since every column modification will require business processes and approvals with the DBA. Couchbase schema flexibility circumvents a lot of these issues.
Greeting!
I have the following problem. I have a table with huge number of rows which I need to search and then group search results by many parameters. Let's say the table is
id, big_text, price, country, field1, field2, ..., fieldX
And we run a request like this
SELECT .... WHERE
[use FULLTEXT index to MATCH() big_text] AND
[use some random clauses that anyway render indexes useless,
like: country IN (1,2,65,69) and price<100]
This we be displayed as search results and then we need to take these search results and group them by a number of fields to generate search filters
(results) GROUP BY field1
(results) GROUP BY field2
(results) GROUP BY field3
(results) GROUP BY field4
This is a simplified case of what I need, the actual task at hand is even more problematic, for example sometimes the first results query does also its own GROUP BY. And example of such functionality would be this site
http://www.indeed.com/q-sales-jobs.html
(search results plus filters on the left)
I've done and still doing a deep research on how MySQL functions and at this point I totally don't see this possible in MySQL. Roughly speaking MySQL table is just a heap of rows lying on HDD and indexes are tiny versions of these tables sorted by the index field(s) and pointing to the actual rows. That's a super oversimplification of course but the point is I don't see how it is possible to fix this at all, i.e. how to use more than one index, be able to do fast GROUP BY-s (by the time query reaches GROUP BY index is completely useless because of range searches and other things). I know that MySQL (or similar databases) have various helpful things such index merges, loose index scans and so on but this is simply not adequate - the queries above will still take forever to execute.
I was told that the problem can be solved by NoSQL which makes use of some radically new ways of storing and dealing with data, including aggregation tasks. What I want to know is some quick schematic explanation of how it does this. I mean I just want to have a quick glimpse at it so that I could really see that it does that because at the moment I can't understand how it is possible to do that at all. I mean data is still data and has to be placed in memory and indexes are still indexes with all their limitation. If this is indeed possible, I'll then start studying NoSQL in detail.
PS. Please don't tell me to go and read a big book on NoSQL. I've already done this for MySQL only to find out that it is not usable in my case :) So I wanted to have some preliminary understanding of the technology before getting a big book.
Thanks!
There are essentially 4 types of "NoSQL", but three of the four are actually similar enough that an SQL syntax could be written on top of it (including MongoDB and it's crazy query syntax [and I say that even though Javascript is one of my favorite languages]).
Key-Value Storage
These are simple NoSQL systems like Redis, that are basically a really fancy hash table. You have a value you want to get later, so you assign it a key and stuff it into the database, you can only query a single object at a time and only by a single key.
You definitely don't want this.
Document Storage
This is one step up above Key-Value Storage and is what most people talk about when they say NoSQL (such as MongoDB).
Basically, these are objects with a hierarchical structure (like XML files, JSON files, and any other sort of tree structure in computer science), but the values of different nodes on the tree can be indexed. They have a higher "speed" relative to traditional row-based SQL databases on lookup because they sacrifice performance on joining.
If you're looking up data in your MySQL database from a single table with tons of columns (assuming it's not a view/virtual table), and assuming you have it indexed properly for your query (that may be you real problem, here), Document Databases like MongoDB won't give you any Big-O benefit over MySQL, so you probably don't want to migrate over for just this reason.
Columnar Storage
These are the most like SQL databases. In fact, some (like Sybase) implement an SQL syntax while others (Cassandra) do not. They store the data in columns rather than rows, so adding and updating are expensive, but most queries are cheap because each column is essentially implicitly indexed.
But, if your query can't use an index, you're in no better shape with a Columnar Store than a regular SQL database.
Graph Storage
Graph Databases expand beyond SQL. Anything that can be represented by Graph theory, including Key-Value, Document Database, and SQL database can be represented by a Graph Database, like neo4j.
Graph Databases make joins as cheap as possible (as opposed to Document Databases) to do this, but they have to, because even a simple "row" query would require many joins to retrieve.
A table-scan type query would probably be slower than a standard SQL database because of all of the extra joins to retrieve the data (which is stored in a disjointed fashion).
So what's the solution?
You've probably noticed that I haven't answered your question, exactly. I'm not saying "you're finished," but the real problem is how the query is being performed.
Are you absolutely sure you can't better index your data? There are things such as Multiple Column Keys that could improve the performance of your particular query. Microsoft's SQL Server has a full text key type that would be applicable to the example you provided, and PostgreSQL can emulate it.
The real advantage most NoSQL databases have over SQL databases is Map-Reduce -- specifically, the integration of a full Turing-complete language that runs at high speed that query constraints can be written in. The querying function can be written to quickly "fail out" of non-matching queries or quickly return with a success on records that meet "priority" requirements, while doing the same in SQL is a bit more cumbersome.
Finally, however, the exact problem you're trying to solve: text search with optional filtering parameters, is more generally known as a search engine, and there are very specialized engines to handle this particular problem. I'd recommend Apache Solr to perform these queries.
Basically, dump the text field, the "filter" fields, and the primary key of the table into Solr, let it index the text field, run the queries through it, and if you need the full record after that, query your SQL database for the specific index you got from Solr. It uses some more memory and requires a second process, but will probably best suite your needs, here.
Why all of this text to get to this answer?
Because the title of your question doesn't really have anything to do with the content of your question, so I answered both. :)
I've been looking at the rise of the NoSql movement and the accompanying rise in popularity of document databases like mongodb, ravendb, and others. While there are quite a few things about these that I like, I feel like I'm not understanding something important.
Let's say that you are implementing a store application, and you want to store in the database products, all of which have a single, unique category. In Relational Databases, this would be accomplished by having two tables, a product and a category table, and the product table would have a field (called perhaps "category_id") which would reference the row in the category table holding the correct category entry. This has several benefits, including non-repetition of data.
It also means that if you misspelled the category name, for example, you could update the category table and then it's fixed, since that's the only place that value exists.
In document databases, though, this is not how it works. You completely denormalize, meaning in the "products" document, you would actually have a value holding the actual category string, leading to lots of repetition of data, and errors are much more difficult to correct. Thinking about this more, doesn't it also mean that running queries like "give me all products with this category" can lead to result that do not have integrity.
Of course the way around this is to re-implement the whole "category_id" thing in the document database, but when I get to that point in my thinking, I realize I should just stay with relational databases instead of re-implementing them.
This leads me to believe I'm missing some key point about document databases that leads me down this incorrect path. So I wanted to put it to stack-overflow, what am I missing?
You completely denormalize, meaning in the "products" document, you would actually have a value holding the actual category string, leading to lots of repetition of data [...]
True, denormalizing means storing additional data. It also means less collections (tables in SQL), thus resulting in less relations between pieces of data. Each single document can contain the information that would otherwise come from multiple SQL tables.
Now, if your database is distributed across multiple servers, it's more efficient to query a single server instead of multiple servers. With the denormalized structure of document databases, it's much more likely that you only need to query a single server to get all the data you need. With a SQL database, chances are that your related data is spread across multiple servers, making queries very inefficient.
[...] and errors are much more difficult to correct.
Also true. Most NoSQL solutions don't guarantee things such as referential integrity, which are common to SQL databases. As a result, your application is responsible for maintaining relations between data. However, as the amount of relations in a document database is very small, it's not as hard as it may sound.
One of the advantages of a document database is that it is schema-less. You're completely free to define the contents of a document at all times; you're not tied to a predefined set of tables and columns as you are with a SQL database.
Real-world example
If you're building a CMS on top of a SQL database, you'll either have a separate table for each CMS content type, or a single table with generic columns in which you store all types of content. With separate tables, you'll have a lot of tables. Just think of all the join tables you'll need for things like tags and comments for each content type. With a single generic table, your application is responsible for correctly managing all of the data. Also, the raw data in your database is hard to update and quite meaningless outside of your CMS application.
With a document database, you can store each type of CMS content in a single collection, while maintaining a strongly defined structure within each document. You could also store all tags and comments within the document, making data retrieval very efficient. This efficiency and flexibility comes at a price: your application is more responsible for managing the integrity of the data. On the other hand, the price of scaling out with a document database is much less, compared to a SQL database.
Advice
As you can see, both SQL and NoSQL solutions have advantages and disadvantages. As David already pointed out, each type has its uses. I recommend you to analyze your requirements and create two data models, one for a SQL solution and one for a document database. Then choose the solution that fits best, keeping scalability in mind.
I'd say that the number one thing you're overlooking (at least based on the content of the post) is that document databases are not meant to replace relational databases. The example you give does, in fact, work really well in a relational database. It should probably stay there. Document databases are just another tool to accomplish tasks in another way, they're not suited for every task.
Document databases were made to address the problem that (looking at it the other way around), relational databases aren't the best way to solve every problem. Both designs have their use, neither is inherently better than the other.
Take a look at the Use Cases on the MongoDB website: http://www.mongodb.org/display/DOCS/Use+Cases
A document db gives a feeling of freedom when you start. You no longer have to write create table and alter table scripts. You simply embed details in the master 'records'.
But after a while you realize that you are locked in a different way. It becomes less easy to combine or aggregate the data in a way that you didn't think was needed when you stored the data. Data mining/business intelligence (searching for the unknown) becomes harder.
That means that it is also harder to check if your app has stored the data in the db in a correct way.
For instance you have two collection with each approximately 10000 'records'. Now you want to know which ids are present in 'table' A that are not present in 'table' B.
Trivial with SQL, a lot harder with MongoDB.
But I like MongoDB !!
OrientDB, for example, supports schema-less, schema-full or mixed mode. In some contexts you need constraints, validation, etc. but you would need the flexibility to add fields without touch the schema. This is a schema mixed mode.
Example:
{
'#rid': 10:3,
'#class': 'Customer',
'#ver': 3,
'name': 'Jay',
'surname': 'Miner',
'invented': [ 'Amiga' ]
}
In this example the fields "name" and "surname" are mandatories (by defining them in the schema), but the field "invented" has been created only for this document. All your app need to don't know about it but you can execute queries against it:
SELECT FROM Customer WHERE invented IS NOT NULL
It will return only the documents with the field "invented".