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MongoDB for Oracle DBA’s Part 1 – Features or Keywords Comparision

Welcome to first Post on MongoDB.  

Given my experience over RDBMS during past decade, exploring the current NOSQL database technologies and keep ourself updated. This will not only help ourselves but also enable us to know where to use this, since majority NOSQL databases are not fit for everything, they are customised and fit for purpose, Like MongoDB have this issues.

  • cannot be used for transactional purpose or commerce/erp applications since its cannot meet the ACID requirements (partial available).
  • MongoDB states that complex queries / joins / aggregations should be avoidable as much as possible.
  • The more the normalized data it suit good but it inherits duplicacy in data.
  • Familiar SQL language is not useful, you should know how to write a query with complex {,}() etc.
  • Schemaless , means not a defined characteristic, means anyone can with any type of data is loaded, means if application required an specific characteristics like datatype check etc.
  • As version evolves, the changes to the database core engine, there is subtle difference between 2.0, 2.6 and 3.2, much more changes and one to keep understand all those.
  • More relies on OS Page cache and flush mechanism no control over but can ask OS to flush often
  • Cannot call as a Fully High Availability Database, hence the new definition BASE, (Basic Availability + Eventual Consistency)
  • Basic Availability - At one point of time, if you loose a shard (with all replica sets) until that shard is available , data is not availabel to business
  • Eventual Consistency - No guarantee that data is shown was consistency, typical example (Thanks to Tim Hall) - Inventory stock in webcommerce can show in two different sessions although the order for that stock is already placed. You will get refund for your order or your order get cancelled after a while
  • Locking - As version changes MongoDB locks also got changed drasitically, initially a database level lock, then a collection level as of 3.2 Document Level lock
  • Locking - Readers lock the writer (shared lock) as of old version
  • MVCC - No undo, until 3.2 , unless use wiredtiger engine
  • Storage - Double sizing due write ahead allocation.

The above is small set of what I understand from small implementations and reading documents. The following is the list of common keywords that we know as Oracle DBA's and what they called in MongoDB.

Oracle MongoDB Description
SGA No SGA MongoDB relies completely on OS Page Cache and Flushing Mechanisms, No Specific SGA concept
BufferPool OS Level Page Cache Controls by OS
Shared Pool Query Cache MongoDB Manages all statements parsed by three query frameworks Query,Aggregation,Sort and the query passes through this engines and parse control by "internalquerycachesize" parameter
Dictionary loaded into memory through .ns file Stores in memory map in the physical ram and use OS level mmap, while mongodb starts this metadata is loaded into memory and map the physical structures through the namespace file 
spfile/pfile /etc/mongod.conf Contains data directory location etc. Port, Sharding, Replication, security informations
StorageEngine mmapv1

wiredtiger

inmemory

This is what I look this is adopted by MySQL terminology MYISAM , INNODB, The Memory Storage Engine

Similarly until MongoDB 3.0 uses default mmapv1, and 3.2 allows wiredtiger as default engine basically (uses read/write or MVCC) and the final one is memory engine

Database Server mongod mongod is database instance
Database Client mongo Like SQLPLUS Shell
Database Listener Router aka mongos In a sharded cluster the mongos instance receives all connections and process
Dictionary Config Servers - mongod MongoD instances roles can be Database, configserver, sharded database
redolog journalfile A file called .j_0 is created in journal folder under storage directory and any write operations writes data to this file before flush for durability purposes, journal file is used for recovery purposes. Once the journal entry is written even mongod crashed without flushing dirty buffers to disk, this journal helps to reapply the statement, well like our instance recovery
database database A database is associated with a namespace file at physical level with default 16MB size and can contain 12000 collections means 12000 tables, and the filename like dbname.ns , which typically contains collections names and indexes details and this file will be mapped in memory
schema None
datafile datafile datafile denotes with database name for example if my database is mydb1 then datafile looks like mydb.1 , mydb.2 etc. Each file starts with 64MB  and new file created doubling the size, So as your database grows the files grows.
alertlog /var/log/mongodb/mongod.log Log for mongodb
Datatypes See table other side
table collection A table is a collection associated with .ns file which loaded into memory
row document
Joins embedded documents or linking
shutdown use admin; db.shutdownServer()
startup service mongod start
expdp mongodump --out <directory>
impdp mongorestore <directory>
v$sysstat db.stats()
v$lock db.currentOp()  shows locks information as well current operations in that node.
v$session db.currentOp()
kill session db.killOp(opid)
v$osstat db.serverStatus()
dba_tables db.collection.stats()
Create Table implicit creation When you insert a first row, the table is implicitly created, no definition is required

Drop Table db.tablename.drop()
Add column db.collection.update( set) To add a column
db.users.update(
{ },
{ $set: { join_date: new Date() } },
{ multi: true }
)
Drop Column db.collection.update( unset) db.users.update(
{ },
{ $unset: { join_date: "" } },
{ multi: true }
)
select db.tablename.find()
select only few columns See other column db.users.find(
{ status: "A" },
{ user_id: 1, status: 1, _id: 0 }
)
select with where see other column db.users.find(
{ status: "A" }
)
insert  Insert will eventually create a table db.users.insert(
{ user_id: "bcd001", age: 45, status: "A" }
)
update  Update a column age > 25 with status C db.users.update(
{ age: { $gt: 25 } },
{ $set: { status: "C" } },
{ multi: true }
)
delete db.tablename.remove()
select with join as of 3.2 version you can use $lookup
Group by db.tablename.group() only available in 3.2 , earlier versions should use db.tablenameaggregate().
count db.tablename.count() Count the documents i.e rows
OEM MMS  GUI tool
RAC Sharding  MongoDB use sharded cluster, each node will have its own partition of data through the key (range,hash,tag)
ASM  Nothing like that  Uses OS Filesystem page cache
Diskgroups  Disks  Uses disks
ASM Mirroring Replica Sets
Clustering Sharding with ReplicaSets Manage BASIC Availability, in event of a node failure with replica sets the data is partially available not completely hence called BASIC Availability not high availability
Private Network No need of private network
addnode sh.addShard("localhost:portnum")
Listener Router or MongoS in a cluster environment to redirect to specific shard for your query, the mongos instance will be used,
Client Mongos mongos is the instance that run client like tns entry
Nothing like that Configservers ConfigServers contains information about data distribution keys and route the request to certain shard. It synchronises the metadata often.
Master-Slave Master-Master MongoDB maintains a router instance called mongos and connect
redolog threads oplog In rac we use threads to detect the instance specific actions, here in MongoDB the high availability means at node level which contain the Primary and replica set with in the node itself, so the Primary and replica set maintain polling mechanism, to ensure all changes replicated to the replica set Oplog will be used for that node only.
datafile resize db.repairDatabase() Reduces the Datafiles for that databases
create user db.addUser db.addUser({ user: "geek",
pwd: "password",
roles: [ "readWrite", "dbAdmin" ]
})
dba_users db.system.users.find()
create database use dbname Eventually Create a new database.
drop database db.dropDatabase()
v$banner db.version()  
voting heartbeat voting between replicatset uses arbiter process to vote between a primary and secondary replica set in two node replicaset, remember not the other shards.
Optimizer  plancache  Plan cache is a program that resides in the memory and process the query
Statistics  Maintain metadata in the extents  Like the datafile header in oracle contains the extent information the table statistics is maintain in extents in the ns file
listener  router aka mongos  Mongos instance works like a listener listen your request, read that request, get the distribution keys from config servers and send request to the nodes.
Port 1521 Port 27017  Default port is 27017
sqlplus mongo  mongo shell
sql tracing db.setProfilingLevel(level, slowms)
  • 0 - logger off
  • 1 - log slow queries
  • 2 - log all queries
cluster status sh.status()
disk replication status rs.status()

rs.printslavereplicationinfo()

Shows how much lag replication is behind primary
Disk Striping sh.enableSharding("students") in ASM striping is done by default to diskgroups, where in mongodb the striping can be enabled at database level and then at collection level striping what is the basis of striping like which column

sh.shardCollection("students.testData", { "x": "hashed", "_id": "hashed" })

startup mongod -f <config filename> mongod is the instance that starts and acts as database node
Limits See the other column Heaps of limitations and varies with versions See, references, https://docs.mongodb.com/manual/reference/limits/

  • 16 MB is maximum row size (aka document)
  • A collection aka table can have 64 indexes
  • A composite index can contain 31 fields aka columns
  • Database Name limited to 64 Characters and case sensitive
  • Embedded documents aka rows i.e 1-N can be upto 100
  • The size of the database in single instance can be subjected to 64TB (archivelog aka journled) 128TB without archive logging (for linux)
  • Cannot rename views
  • Normal write operations from app cannot be more than 1000 unless use mongoshell or bulk () operations
  • Index field must not contain more than 1024 bytes
  • 12 nodes in a replicat set and can have 7 voting nodes
  • Striping aka Sharding of a table to distribute data to other nodes should not be more than 256gb
  • Group/Sort queries fails if it exceeds more than 10% of memory
  • Max connections can be 20K (hardcoded)
  • The namesspace file (a metadata file for collections or database) cannot have more than 24000 collections, hence in simple words each database cannot contain more than 24000 objects i.e collections + indexes
Flush SQL ID db.runCommand(

{

planClearCache: "orders"

})

Explain plan db.tablename.find(query).explain You can also explain by query and specific where condition see documentation

Next Post is on Installing & Creating Standalone MongoDB Database

-Thanks

Geek DBA

1 comment to MongoDB for Oracle DBA’s Part 1 – Features or Keywords Comparision

  • RAJEEV SINHA

    Super Comparison document. Kudos to the writer.. Keep good work on. very useful for beginner.