I know (or think I know) that WiredTiger stores all non-index data as table-like structures. MongoDB somehow stores NoSQL BSON documents in this structure, and supports searches and indexes on specific columns. How does it do this? In other words, what is the schema by which MongoDB stores data in WiredTiger?
Related
MongoDB WiredTiger offers LSMT for storage. Great, so in memory it maintains a balanced search tree which is flushed to disk depending on configuration (time or size). But q is, how is the data stored on disk? LSMT in Cassandra/HBase are stored as immutable files. These are compacted from time to time. Data is inserted/updated/deleted as cells which are part of a logical distributed dictionary. So each cell is identified by key, column name and version.
But MongoDB uses BSON. This is a single document.so q arises:
Does MongoDB break down BSON into cells and updates/versions them? Most unlikely, since BSON was designed for disk storage.
In that case, how is the memtable (balanced search tree) update the BSON file? Is the BSON file mutable? LSMT uses immutable files in Cassandra/HBase.
In general, how does MongoDB WT do updates? Mutable files on disk, or immutable, and the effort for index management? Since MongoDB offers lots of indexing types.
Thanks
I have a large geojson feature collection which is over 16MB. I am hoping to insert the data into MongoDB so that I can utilize the geospatial functionality that MongoDB offers ($geoIntersects, $geoWithin, etc). Due to the large size of the file, I cannot store the data in one MongoDB document.
I have used GridFS to break the file up into several chunks within MongoDB but I am now unsure whether I can now utilize the geospatial features that I would like to.
Does anyone know if this is possible and if so whats the best way to do something like this?
One way you should be able to achieve what you are describing is to extract the data to be indexed into a separate collection, and add indexes on that collection.
GridFS essentially takes the data, splits it into 16 MB chunks or less and stores each chunk as a document. I don't see how the chunks would be supported by a geo index.
NoSQL term has 4 categories.
Key\value stores
Document oriented
Graph
Column oriented.
From my point of view all these data modeling has same definition, What are differences?
Key\value database maintains data in structure like object in OOP. having access to data is base on unique key.
Column oriented is an approach like key\value! But in key\value, you cant access to value by query. I mean, queries are key-based.
Compare 1st & 2nd picture from 2 different categories.
Document oriented stores data in collections, something like rows. Having access to data is base on unique key. The collections store data like key\value. However, you can access data by value.
As you can see, In these 3 categories, we define a unique key for specify a unique object & some pairs of key\value for more information
Graph db is a little different.
So, what are differences in definition & in real-world?
Document databases pair each key with a complex data structure known as a document. Documents can contain many different key-value pairs, or key-array pairs, or even nested documents.
Graph stores are used to store information about networks of data, such as social connections. Graph stores include Neo4J and Giraph.
Key-value stores are the simplest NoSQL databases. Every single item in the database is stored as an attribute name (or 'key'), together with its value. Examples of key-value stores are Riak and Berkeley DB. Some key-value stores, such as Redis, allow each value to have a type, such as 'integer', which adds functionality.
Wide-column stores such as Cassandra and HBase are optimized for queries over large datasets, and store columns of data together, instead of rows.
for more, follow this link on MongoDB
what I have ?
I have data of 'n' department
each department has more than 1000 datasets
each datasets has more than 10,000 csv files(size greater than 10MB) each with different schema.
This data even grow more in future
What I want to DO?
I want to map this data into mongodb
What approaches I used?
I can't map each datasets to a document in mongo since it has limit of 4-16MB
I cannot create collection for each datasets as max number of collection is also limited (<24000)
So finally I thought to create collection for each department , in that collection one document for each record in csv file belonging to that department.
I want to know from you :
will there be a performance issue if we map each record to document?
is there any max limit for number of documents?
is there any other design i can do?
will there be a performance issue if we map each record to document?
mapping each record to document in mongodb is not a bad design. You can have a look at FAQ at mongodb site
http://docs.mongodb.org/manual/faq/fundamentals/#do-mongodb-databases-have-tables .
It says,
...Instead of tables, a MongoDB database stores its data in collections,
which are the rough equivalent of RDBMS tables. A collection holds one
or more documents, which corresponds to a record or a row in a
relational database table....
Along with limitation of BSON document size(16MB), It also has max limit of 100 for level of document nesting
http://docs.mongodb.org/manual/reference/limits/#BSON Document Size
...Nested Depth for BSON Documents Changed in version 2.2.
MongoDB supports no more than 100 levels of nesting for BSON document...
So its better to go with one document for each record
is there any max limit for number of documents?
No, Its mention in reference manual of mongoDB
...Maximum Number of Documents in a Capped Collection Changed in
version
2.4.
If you specify a maximum number of documents for a capped collection
using the max parameter to create, the limit must be less than 232
documents. If you do not specify a maximum number of documents when
creating a capped collection, there is no limit on the number of
documents ...
is there any other design i can do?
If your document is too large then you can think of document partitioning at application level. But it will have high computation requirement at application layer.
will there be a performance issue if we map each record to document?
That depends entirely on how you search them. When you use a lot of queries which affect only one document, it is likely even faster that way. When a higher document-granularity results in a lot of document-spanning queries, it will get slower because MongoDB can't do that itself.
is there any max limit for number of documents?
No.
is there any other design i can do?
Maybe, but that depends on how you want to query your data. When you are content with treating files as a BLOB which is retrieved as a whole but not searched or analyzed on the database level, you could consider storing them on GridFS. It's a way to store files larger than 16MB on MongoDB.
In General, MongoDB database design doesn't depend so much on what and how much data you have, but rather on how you want to work with it.
I know that MongoDB accepts and retrieves records as JSON/BSON objects, but how does it actually store these files on disk? Are they stored as a collection of individual *.json files or as one large file? I have a hunch as to the latter, since the MongoDB docs state that it works best on systems with ext4/xfs, which are better at handling large files. Can anyone confirm?
A given mongo database is broken up into a series of BSON files on disk, with increasing size up to 2GB. BSON is its own format, built specifically for MongoDB.
These slides should answer all of your questions:
http://www.slideshare.net/mdirolf/inside-mongodb-the-internals-of-an-opensource-database
MongoDB stores the data on the disk as BSON in your data path directory, which is usually /data/db. There should be two files per collection there, collection.0, which stores the data (and that integer is then incremented as needs be) and collection.ns which stores the namespacing metadata for the collection.
Detailed documentation of the BSON format can be found here: http://bsonspec.org/
Up to mongodb 3.0
http://blog.mongolab.com/2014/01/how-big-is-your-mongodb/
If you turn on wiredtiger storage engine in MongoDB 3.0 it will use wiredtiger storage model
http://docs.mongodb.org/v3.0/core/storage/#storage-wiredtiger