MongoDB Tutorial 2 : Terminlology



  1. _id – This is a field required in every MongoDB document. The _id field represents a unique value in the MongoDB document. The _id field is like the document's primary key. If you create a new document without an _id field, MongoDB will automatically create the field. So for example, if we see the example of the above customer table, Mongo DB will add a 24 digit unique identifier to each document in the collection.
  2. Collection – This is a grouping of MongoDB documents. A collection is the equivalent of a table which is created in any other RDMS such as Oracle or MS SQL. A collection exists within a single database. As seen from the introduction collections don't enforce any sort of structure.
  3. Cursor – This is a pointer to the result set of a query. Clients can iterate through a cursor to retrieve results.
  4. Database – This is a container for collections like in RDMS wherein it is a container for tables. Each database gets its own set of files on the file system. A MongoDB server can store multiple databases.
  5. Document - A record in a MongoDB collection is basically called a document. The document in turn will consist of field name and values.
  6. Field - A name-value pair in a document. A document has zero or more fields. Fields are analogous to columns in relational databases.
  7. JSON – This is known as JavaScript Object Notation. This is a human-readable, plain text format for expressing structured data. JSON is currently supported in many programming languages.


Just a quick note on the key difference between the _id field and a normal collection field. The _id field is used to uniquely identify the documents in a collection and is automatically added by MongoDB when the collection is created.

Comments

Post a Comment

Popular posts from this blog

Hive Tutorial 31 : Analytic Functions

Image
Hadoop Hive analytic functions compute an aggregate value that is based on a group of rows. A Hadoop Hive HQL analytic function works on the group of rows and ignores the NULL in the data if you specify Hadoop Hive COUNT Analytic Function Returns number of rows in query or group of rows. Syntax: COUNT(column reference | value expression | *) over(window_spec) For Example; select pat_id, dept_id, count(*) over (partition by dept_id order by dept_id asc) as pat_cnt from patient; at_id dept_id pat_cnt 6 111 4 2 111 4 5 111 4 1 111 4 4 222 3 5 222 3 3 222 3 7 333 1 8 444 1 Hadoop Hive SUM Analytic Function Just like count function, sum Hive analytic function is used to compute the sum of columns or expression. Sum analytic function is used to compute the sum of all rows of table or rows within the groups. Syntax: SUM(column | expression) OVER( window_spec) For example: Calculate sum insured amount of all patients within each department...
Read more

Hive Tutorial 37 : Performance Tuning

Performance plays key role in big data related projects as they deals which huge amount of data. So when you are using Hive if you keep few things in mind then we can see dramatic change in the performance.  Performance tuning in hive: Partitions Bucketing File formats Compression Sampling Tez Vectorization Parallel execution CBO Partitions : The concept of partitioning in Hive is very similar to what we have in RDBMS. A table can be partitioned by one or more keys. This will determine how the data will be stored in the table. For example, if a table has two columns, id, name and age; and is partitioned by age, all the rows having same age will be stored together. So when we try to query based on age range, then hive will retrieve the data by going into particular folders instead of parsing through whole data. /hdfs/user/tablename/age/10 /hdfs/user/tablename/age/11 Bucketing : Bucketing is more efficient for sampling,data will be segre...
Read more