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NoSQL
continued
CMSC 461
Michael Wilson
MongoDB
 MongoDB


is another NoSQL solution
Provides a bit more structure than a solution
like Accumulo
Data is stored as BSON (Binary JSON)
 Binary
 Allows
encoded JSON, extends JSON
storage of large amounts of data
SQL vs. MongoDB
 SQL
has databases, tables, rows, columns
 Monbo has databases, collections,
documents, fields
 Both have primary keys, indexes
 Collection structures are not enforced
heavily

Inserts automatically create schemas
Interacting with MongoDB
 Multiple

Switch databases
 use


newDb
New databases will be stored after an insert
 Create

databases within MongoDB
collection
db.createCollection(“collectionName”)
Not necessary, collections are implicitly
created on insert
BSON
 MongoDB



uses BSON very heavily
Binary JSON
Like JSON with a binary serialization method
Has extensions so that it can represent data
types that JSON cannot
 Used
to represent documents, provide
input to queries
Selects/queries

In MongoDB, querying typically consists of
providing an appropriately crafted BSON

SELECT * FROM collectionName


SELECT * FROM collectionName WHERE field =
value


db.collectionName.find( {field: value} )
SELECT * FROM collectionName WHERE field > 5


db.collectionName.find()
db.collectionName.find( {field: {$gt: 5} } )
Other functions that take a query argument
have queries that are formatted this way
Interacting with MongoDB

Insert


db.collectionName.insert( {queryBSON} )
Update

db.collectionName.update( {queryBSON},
{updateBSON}, {optionBSON} )

updateBSON



Set field to 5: {$set: {field: 5}}
Increment field by 1 {$inc: {field: 1}}
optionBSON

Options that determine whether or not to create
new documents, update more than one document,
write concerns
Interacting with MongoDB
 Delete

db.collectionName.remove( {queryBSON} )
Apache Hive
 Also
runs on Hadoop, uses HDFS as a data
store
 Queryable like SQL

Using an SQL-inspired language, HiveQL
Hive data organization
 Databases
 Tables
 Partitions

Tables are broken down into partitions
 Partition
keys allow data to be stored into
separate data files on HDFS
 Can query on particular partitions
 Buckets

Can bucket by column to sample data
Purpose of Hive
 Provide
data

NOT to be used for real time queries like
Postgres or Oracle
 Hive

analytics, query large volumes of
queries take forever
Partitions and buckets can help reduce this
amount of time
Hive queries
 Hive
queries actually generate
MapReduce jobs

MapReduce jobs take a while to set up and
run
 MapReduce
jobs can be run manually,
but for structured data and analytics, Hive
can be used