1 mongo db mongodb (from "humongous) is a scalable, high- performance, open source,...

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Mongo DB

MongoDB (from "humongous“) is a scalable, high-performance, open source, document-oriented database. Written in C++.

Home: http://www.mongodb.org/ Support by http://www.10gen.com/ Production Deploy

http://www.mongodb.org/display/DOCS/Production+Deployments

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Agenda

Getting Up to Speed with MongoDB ( key features)

Developing with MongoDB (start & shutdown & connect & query & DML)

Advanced Usage ( index & Aggregation, GridFS) Administration ( admin,replication,sharding) MISC (BJSON;internal)

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Getting Up to Speed with MongoDB

Key Features of MongoDB1. document-oriented

2. schema-free

Design Philosophy

Different from Relation database easy & simple admin/dev : no-transaction, no-relation, no-

duration, no-SQL

Different from key-value database tons of functions: indexing,Aggregation (MapReduce etc),Fixed-

size collections,File storage,replication

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Key Features of MongoDB

1. Document-oriented (multiple key/value pairs)

{ "_id" : ObjectId("4d9a2fa7640cde2b218c6f65"), "version" : 1300363306, "evenOrOdd" : 0, "siteId" : 0 }

{ "_id" : ObjectId("4d9cd50d2a98297726eeda5b"), "prefix" : "craft fl", "res" : { "sug" : [ "craft flowers" ], "categories" : [ [ 14339, "Crafts" ] ] } }

2. Instance --1:X-- database -- 1:X– collection –1:X – document

3. schema-free

• Use test

• Db.test.insert({ "version" : 1300363306, "evenOrOdd" : 0, "siteId" : 0 })

• Db.test.insert({"prefix" : "craft fl", "res" : { "sug" : [ "craft flowers" ], "categories" : [ [ 14339, "Crafts" ] ] } })

• Db.test.insert({“name”:”binzhang’});

• Db.test.ensureIndex({“name”,”1”})

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Design Philosophy

1. Databases are specializing - the "one size fits all" approach no longer applies. MongoDB is bettween in-memory key-value and relational persistent database.

2. By reducing transactional semantics the db provides, one can still solve an interesting set of problems where performance is very important, and horizontal scaling then becomes easier. The simpler, the faster.

3. The document data model (JSON/BSON) is easy to code to, easy to manage (schemaless), and yields excellent performance by grouping relevant data together internally. But waste a bit space.

4. A non-relational approach is the best path to database solutions which scale horizontally to many machines. Easy to scale out for in-complex application.

5. While there is an opportunity to relax certain capabilities for better performance, there is also a need for deeper functionality than that provided by pure key/value stores.

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Different from Relation database

1. easy & simple admin/dev • Kill –INT to shutdown instance

2. no-transaction3. no-relation4. no-duration

KILL -9 would corrupt database and need to repair when start up next time.

5. no-SQL MongoDB comes with a JavaScript shell that allows

interaction with MongoDB instance from the command line.

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Different from key-value database1. Datatypes: null,boolean,32-bit integer,64 bit integer,64 bit floating

point number, string, object_id,date,regular expression,code,binary data,undefined,array,embedded document etc

2. Indexing: unique index,combine index, geospatial indexing etc

3. Aggregation (MapReduce etc): group distinct etc

4. Fixed-size collections: Capped collections are fixed in size and are useful for certain types of data, such as logs.

5. File storage: a protocol for storing large files, uses subcollections to store file metadata separately from content chunks

6. Replication: include master-slave mode and replicate-set mode

7. Security : simple authorization.

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Agenda

Getting Up to Speed with MongoDB ( Summary : Getting Up to Speed with MongoDB ( Summary : document oriented & schema-free )document oriented & schema-free )


Advanced Usage ( index & Aggregation, GridFS) Administration ( admin,replication,sharding) MISC

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Developing with MongoDB

Continue. Start MongoDB connect Query DML ( create, insert, update, delete, drop ) Stop cleanly

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Start MongoDB

Mkdir /MONGO/data01

/opt/mongo/bin/mongod --logpath /MONGO/log01/server_log.txt --logappend --fork --cpu --dbpath /MONGO/data01 --replSet autocomplete

Fri Apr 1 14:37:08 [initandlisten] MongoDB starting : pid=10799 port=27017 dbpath=/MONGO/data01 64-bit

Fri Apr 1 14:37:08 [initandlisten] db version v1.8.0, pdfile version 4.5Fri Apr 1 14:37:08 [initandlisten] git version: 9c28b1d608df0ed6ebe791f63682370082da41c0Fri Apr 1 14:37:08 [initandlisten] build sys info: Linux bs-linux64.10gen.cc 2.6.21.7-

2.ec2.v1.2.fc8xen #1 SMP Fri Nov 20 17:48:28 EST 2009 x86_64 BOOST_LIB_VERSION=1_41

Fri Apr 1 14:37:08 [initandlisten] waiting for connections on port 27017Fri Apr 1 14:37:08 [websvr] web admin interface listening on port 28017

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Developing with MongoDB Connect to MongoD

$opt/mongo/bin/mongo

MongoDB shell version: 1.8.0

connecting to: test

autocomplete:PRIMARY> exit

Bye

usage: /opt/mongo/bin/mongo [options] [db address] [file names (ending in .js)]

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Developing with MongoDB MongoDB Shell

MongoDB comes with a JavaScript shell that allows interaction with a MongoDB instancefrom the command line.

Query – find()

db.c.find() returns everything in the collection c. db.users.find({"age" : 27}) where the value for "age" is 27 db.users.find({}, {"username" : 1, "email" : 1}) if you are interested only in the

"username" and "email" keys db.users.find({}, {"fatal_weakness" : 0}) never want to return the

"fatal_weakness" key db.users.find({}, {"username" : 1, "_id" : 0}) db.users.find({"age" : {"$gte" : 18, "$lte" : 30}}) db.raffle.find({"ticket_no" : {"$in" : [725, 542, 390]}}) db.c.find({"z" : {"$in" : [null], "$exists" : true}}) db.users.find({"name" : /joe/i})

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Behind Find() : Cursor The database returns results from find using a cursor. The client-side implementations of cursors generally allow you

to control a great deal about the eventual output of a query.

> for(i=0; i<100; i++) {

... db.c.insert({x : i});

... }

> var cursor = db.collection.find(); > while (cursor.hasNext()) { ... obj = cursor.next(); ... // do stuff ... }

> var cursor = db.people.find();> cursor.forEach(function(x) {... print(x.name);... });adammattzak

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Developing with MongoDBBehind Find() : Cursor continue

Getting Consistent Results? var cursor = db.myCollection.find({country:'uk'}).snapshot();

A fairly common way of processing data is to pull it out of MongoDB, change it in some way, and then save it again:

cursor = db.foo.find();

while (cursor.hasNext()) {

var doc = cursor.next();

doc = process(doc);

db.foo.save(doc);

}

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Developing with MongoDB Create collection db.foo.insert({"bar" : "baz"})

Insert db.foo.insert({"bar" : "baz"})

Update

db.users.update({"_id" : ObjectId("4b253b067525f35f94b60a31")}, ... {"$set" : {"favorite book" : "war and peace"}})

Deletedb.users.remove()db.mailing.list.remove({"opt-out" : true})

Drop collection db.foo.drop();

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Developing with MongoDB DML continue : Safe Operation

1. MongoDB does not wait for a response by default when writing to the database. Use the getLastError command to ensure that operations have succeeded.

2. The getLastError command can be invoked automatically with many of the drivers when saving and updating in "safe" mode (some drivers call this "set write concern").

db.$cmd.findOne({getlasterror:1}) db.runCommand("getlasterror") db.getLastError()

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Stop MongoDB

kill -2 10014 (SIGINT) or kill 10014 (SIGTERM).

1. wait for any currently running operations or file preallocations to finish (this could take a moment)

2. close all open connections 3. flush all data to disk4. halt.

use the shutdown command

> use adminswitched to db admin> db.shutdownServer();server should be down...

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Agenda

Getting Up to Speed with MongoDB ( Summary : Getting Up to Speed with MongoDB ( Summary : document oriented & schema-free )document oriented & schema-free )

Developing with MongoDB (Summary: find())Developing with MongoDB (Summary: find()) Advanced Usage ( index & Aggregation, GridFS) Administration ( admin,replication,sharding) MISC

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MongoDB Advanced Usage

Advanced Usage Index Aggregation MapReduce Database commands Capped Collections GridFS: Storing Files

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Index MongoDB’s indexes work almost identically to typical relational database

indexes, Index optimization for MySQL/Oracle/SQLite will apply equally well to

MongoDB. if an index has N keys, it will make queries on any prefix of those keys

fast Example

db.people.find({"username" : "mark"}) db.people.ensureIndex({"username" : 1})

db.people.find({"date" : date1}).sort({"date" : 1, "username" : 1}) db.ensureIndex({"date" : 1, "username" : 1})

db.people.find({"username" : "mark"}).explain()

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Index continue Indexes can be created on keys in embedded documents in the same way

that they are created on normal keys. Indexing for Sorts : Indexing the sort allows MongoDB to pull the sorted data

in order, allowing you to sort any amount of data without running out of memory.

Index Nameing rule: keyname1_dir1_keyname2_dir2_..._keynameN_dirN, where keynameX is the index’s key and dirX is the index’s direction (1 or -1).

db.blog.ensureIndex({"comments.date" : 1}) db.people.ensureIndex({"username" : 1}, {"unique" : true}) db.people.ensureIndex({"username" : 1}, {"unique" : true, "dropDups" : true})

autocomplete:PRIMARY> db.system.indexes.find(){ "name" : "_id_", "ns" : "test.fs.files", "key" : { "_id" : 1 }, "v" : 0 }{ "ns" : "test.fs.files", "key" : { "filename" : 1 }, "name" : "filename_1", "v" : 0 }{ "name" : "_id_", "ns" : "test.fs.chunks", "key" : { "_id" : 1 }, "v" : 0 }{ "ns" : "test.fs.chunks", "key" : { "files_id" : 1, "n" : 1 }, "name" : "files_id_1_n_1",

"v" : 0 }

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Index continue : explain() explain will return information about the indexes used for the query (if any) and stats

about timing and the number of documents scanned.

autocomplete:PRIMARY> db.users.find({"name":"user0"}).explain(){ "cursor" : "BtreeCursor name_1", "nscanned" : 1, "nscannedObjects" : 1, "n" : 1, "millis" : 0, "nYields" : 0, "nChunkSkips" : 0, "isMultiKey" : false, "indexOnly" : false, "indexBounds" : { "name" : [ [ "user0", "user0" ] ] }}

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Index continue : hint() If you find that Mongo is using different indexes than you want it to

for a query, you can force it to use a certain index by using hint.

db.c.find({"age" : 14, "username" : /.*/}).hint({"username" : 1, "age" : 1})

Index continue : change index db.runCommand({"dropIndexes" : "foo", "index" : "alphabet"})

db.people.ensureIndex({"username" : 1}, {"background" : true}) Using the {"background" : true} option builds the index in the

background, while handling incoming requests. If you do not include the background option, the database will block all other requests while the index is being built.

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MongoDB Advanced Usage Advanced Usage

Index Aggregation

db.foo.count()db.foo.count({"x" : 1})db.runCommand({"distinct" : "people", "key" : "age"})

Group

{"day" : "2010/10/03", "time" : "10/3/2010 03:57:01 GMT-400", "price" : 4.23}{"day" : "2010/10/04", "time" : "10/4/2010 11:28:39 GMT-400", "price" : 4.27}{"day" : "2010/10/03", "time" : "10/3/2010 05:00:23 GMT-400", "price" : 4.10}{"day" : "2010/10/06", "time" : "10/6/2010 05:27:58 GMT-400", "price" : 4.30}{"day" : "2010/10/04", "time" : "10/4/2010 08:34:50 GMT-400", "price" : 4.01}

db.runCommand({"group" : {... "ns" : "stocks",... "key" : "day",... "initial" : {"time" : 0},... "$reduce" : function(doc, prev) {... if (doc.time > prev.time) {... prev.price = doc.price;... prev.time = doc.time;... } }}})

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Mapreduce It is a method of aggregation that can be easily parallelized across

multiple servers. It splits up a problem, sends chunks of it to different machines, and lets each machine solve its part of the problem. When all of the machines are finished, they merge all of the pieces of the solution back into a full solution.\

Example: Finding All Keys in a Collection>map = function() {... for (var key in this) {... emit(key, {count : 1});... }};

> mr = db.runCommand({"mapreduce" : "foo", "map" : map, "reduce" : reduce})

> db[mr.result].find()

> reduce = function(key, emits) {... total = 0;... for (var i in emits) {... total += emits[i].count; }... return {"count" : total};... }

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MongoDB Advanced Usage Database commands

Commands implement all of the functionality that doesn’t fit neatly into “create, read, update, delete.”

Example:> db.runCommand({"drop" : "test"});{ "errmsg" : "ns not found", "ok" : false }

It equals querying $cmd internal collections.

>db.$cmd.findOne({"drop" : "test"});

Show all commands >db.listCommands()

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MongoDB Advanced Usage Capped Collections1. capped collections automatically age-out the

oldest documents as new documents are inserted.

2. Documents cannot be removed or deleted (aside from the automatic age-out described earlier), and updates that would cause documents to move (in general updates that cause documents to grow in size) are disallowed.

3. inserts into a capped collection are extremely fast.

4. By default, any find performed on a capped collection will always return results in insertion order.

5. ideal for use cases like logging.

6. Replication use capped collection as OpLog.

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MongoDB Advanced Usage GridFS: Storing Files

GridFS is a mechanism for storing large binary files in MongoDB. Why using GridFS

• Using GridFS can simplify your stack. If you’re already using MongoDB, GridFSobviates the need for a separate file storage architecture.

• GridFS will leverage any existing replication or autosharding that you’ve set up forMongoDB, so getting failover and scale-out for file storage is easy.

• GridFS can alleviate some of the issues that certain filesystems can exhibit whenbeing used to store user uploads. For example, GridFS does not have issues withstoring large numbers of files in the same directory.

• You can get great disk locality with GridFS, because MongoDB allocates data filesin 2GB chunks.

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MongoDB Advanced Usage GridFS: example

$ echo "Hello, world" > foo.txt$ ./mongofiles put foo.txtconnected to: 127.0.0.1added file: { _id: ObjectId('4c0d2a6c3052c25545139b88'),filename: "foo.txt", length: 13, chunkSize: 262144,uploadDate: new Date(1275931244818),md5: "a7966bf58e23583c9a5a4059383ff850" }done!

$ ./mongofiles listconnected to: 127.0.0.1foo.txt 13

$ rm foo.txt$ ./mongofiles get foo.txtconnected to: 127.0.0.1done write to: foo.txt

$ cat foo.txtHello, world

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MongoDB Advanced Usage GridFS: internal

The basic idea behind GridFS is that we can store large files by splitting them up into chunks and storing each chunk as a separate document.

autocomplete:PRIMARY> show collectionsfs.chunksfs.filessystem.indexes

autocomplete:PRIMARY> db.fs.chunks.find(){ "_id" : ObjectId("4db258ae05a23484714d58ad"), "files_id" : ObjectId("4db258ae39ae206d1114d6e4"), "n" :

0, "data" : BinData(0,"SGVsbG8sbW9uZ28K") }{ "_id" : ObjectId("4db258d305a23484714d58ae"), "files_id" : ObjectId("4db258d37858d8bb53489eea"), "n" :

0, "data" : BinData(0,"SGVsbG8sbW9uZ28K") }{ "_id" : ObjectId("4db2596d05a23484714d58af"), "files_id" : ObjectId("4db2596d4fefdd07525ef166"), "n" : 0,

"data" : BinData(0,"SGVsbG8sbW9uZ28K") }

autocomplete:PRIMARY> db.fs.files.find(){ "_id" : ObjectId("4db258ae39ae206d1114d6e4"), "filename" : "file1.txt", "chunkSize" : 262144, "uploadDate" :

ISODate("2011-04-23T04:42:22.546Z"), "md5" : "c002dec1a1086442b2aa49c2b6e48884", "length" : 12 }{ "_id" : ObjectId("4db258d37858d8bb53489eea"), "filename" : "file2.txt", "chunkSize" : 262144, "uploadDate" :

ISODate("2011-04-23T04:42:59.851Z"), "md5" : "c002dec1a1086442b2aa49c2b6e48884", "length" : 12 }{ "_id" : ObjectId("4db2596d4fefdd07525ef166"), "filename" : "file2.txt", "chunkSize" : 262144, "uploadDate" :

ISODate("2011-04-23T04:45:33.771Z"), "md5" : "c002dec1a1086442b2aa49c2b6e48884", "length" : 12 }

autocomplete:PRIMARY> db.system.indexes.find(){ "ns" : "test.fs.files", "key" : { "filename" : 1 }, "name" : "filename_1", "v" : 0 }{ "ns" : "test.fs.chunks", "key" : { "files_id" : 1, "n" : 1 }, "name" : "files_id_1_n_1", "v" : 0 }

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Advanced Usage review Index : almost same as oracle Aggregation MapReduce : built-in Database commands : db.listCommands() Capped Collections : suitable for log GridFS: Storing Files : built-in document oriented

Others Geospatial Indexing Database References Server-Side Scripting

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DBA on MongonDB Administration ( admin,replication,sharding)

Monitoring

Security and Authentication

Backup and Repair

Master-Slave Replication

Replication-set

Sharding

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DBA on MongonDB

Easy Monitoring Using the Admin Interface db.runCommand({"serverStatus" : 1}) mongostat Third-Party Plug-Ins

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Using the Admin Interface

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db.runCommand({"serverStatus" : 1})

db.runCommand({"serverStatus" : 1}) { "version" : "1.5.3", "uptime" : 166, "localTime" : "Thu Jun 10 2010 15:47:40 GMT-

0400 (EDT)", "globalLock" : { "totalTime" : 165984675, "lockTime" : 91471425, "ratio" : 0.551083556358441 }, "mem" : { "bits" : 64, "resident" : 101, "virtual" : 2824, "supported" : true, "mapped" : 336 }, "connections" : { "current" : 141, "available" : 19859 }, "extra_info" : { "note" : "fields vary by platform" }, "indexCounters" : { "btree" : { "accesses" : 1563, "hits" : 1563, "misses" : 0,

"backgroundFlushing" : { "flushes" : 2, "total_ms" : 44, "average_ms" : 22, "last_ms" : 36, "last_finished" : "Thu Jun 10 2010

15:46:54 GMT-0400 (EDT)" }, "opcounters" : { "insert" : 38195, "query" : 8874, "update" : 4058, "delete" : 389, "getmore" : 888, "command" : 17731 }, "asserts" : { "regular" : 0, "warning" : 0, "msg" : 0, "user" : 5054, "rollovers" : 0 },

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mongostat Fields inserts - # of inserts per second query - # of queries per second update - # of updates per second delete - # of deletes per second getmore - # of get mores (cursor batch) per

second command - # of commands per second flushes - # of fsync flushes per second mapped - amount of data mmaped (total data size)

megabytes visze - virtual size of process in megabytes res - resident size of process in megabytes faults - # of pages faults per sec (linux only) locked - percent of time in global write lock idx miss - percent of btree page misses (sampled) qr|qw - queue lengths for clients waiting (read|

write) ar|aw - active clients (read|write) netIn - network traffic in - bits netOut - network traffic out - bits conn - number of open connections

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DBA on MongonDB

Security and Authentication

1. Each database in a MongoDB instance can have any number of users.

2. only authenticated users of a database are able to perform read or write operations on it.

3. A user in the admin database can be thought of as a superuser

4. Need to start MongoDB with “--auth” option to enable authentication.

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Backup on MongonDB1. Data File Cold Backup

kill –INT mongod; copy --dbpath2. mongodump (exp) and mongorestore (imp)3. fsync and Lock4. Slave Backup

> use adminswitched to db admin> db.runCommand({"fsync" : 1, "lock" : 1});{"info" : "now locked against writes, use db.$cmd.sys.unlock.findOne() to unlock","ok" : 1}

Do mongodump

> db.$cmd.sys.unlock.findOne();{ "ok" : 1, "info" : "unlock requested" }

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Repair MongonDB

1.Need to repair databases after an unclean shutdown ( kill -9 )

**************old lock file: /data/db/mongod.lock. probably means unclean

shutdownrecommend removing file and running --repairsee: http://dochub.mongodb.org/core/repair for more information*************

2. All of the documents in the database are exported and then immediately imported, ignoring any that are invalid. Then rebuild indexes.

3. Take a long time while data-set is humongous4. Repairing a database will also perform a

compaction.5. db.repairDatabase() can repair single database

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DBA on MongonDB

Replication Master-Slave Replication Replication-set

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$ mkdir -p ~/dbs/master$ ./mongod --dbpath ~/dbs/master --port 10000 --master

$ mkdir -p ~/dbs/slave$ ./mongod --dbpath ~/dbs/slave --port 10001 --slave --source localhost:10000

1. Scale read2. Backup on Slave3. Process data on Slave4. DR

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How it works? The Oplogoplog.$main a capped collection in local database.

• ts Timestamp for the operation. The timestamp type is an internal type used to track when operations are performed. It is composed of a 4-byte timestamp and a 4-byte incrementing counter.

• op Type of operation performed as a 1-byte code (e.g., “i” for an insert).• ns Namespace (collection name) where the operation was performed.• o Document further specifying the operation to perform. For an insert,

this would be the document to insert.

1. Slave first starts up, it will do a full sync of the data on the master node.

2. After the initial sync is complete, the slave will begin querying the master’s oplog and applying operations in order to stay up-to-date. “async”

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Replication on MongonDB

Replication-set

1. A replica set is basically a master-slave cluster with automatic failover.

2. One master, some secondary (slave)

3. One secondary is elected by the cluster and may change to another node if the current master goes down.

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Replication on MongonDB

Setup Replication-set1. Option --replSet is name for this replica set.

$ ./mongod --dbpath ~/dbs/node1 --port 10001 --replSet autocomplete/slcdbx1005:10002

We start up the other server in the same way:


If we wanted to add a third server, we could do so with either of these commands:


$ ./mongod --dbpath ~/dbs/node3 --port 10003 –replSet autocomplete/slcdbx1005:10001, slcdbx1006:10002

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Replication-set failover

•standard a full copy of data & voting & ready to be primary•passive a full copy of data & voting •arbiter voting & no data replicated

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MongonDB Auo-sharding

Sharding : splitting data up and storing different portions of thedata on different machines.

1. Manualy sharding: The application code manages storing different data on different servers and querying against the appropriate server to get data back.

2. Auto Sharding : The cluster handles splitting up data and rebalancing automatically.

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Auto sharding

When to shard?

1. You’ve run out of disk space on your current machine.

2. You want to write data faster than a single mongod can handle.

3. You want to keep a larger proportion of data in memory to improve performance.

4. DR5. Failover automatically

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Auto sharding

Component of MongoDB sharding?• Config server$ mkdir -p ~/dbs/config$ ./mongod --dbpath ~/dbs/config --port 20000

• Mongos (router)$ ./mongos --port 30000 --configdb localhost:20000

• Sharding ( usually replication-set)

$ mkdir -p ~/dbs/shard1$ ./mongod --dbpath ~/dbs/shard1 --port 10000

Mongos> db.runCommand({addshard : "localhost:10000", allowLocal : true})

{"added" : "localhost:10000","ok" : true}

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Pre sharding a table

Determine a shard key

1. define how we distribute data. 2. MongoDB's sharding is order-preserving; adjacent data by shard key tends to be on the same

server.3. The config database stores all the metadata indicating the location of data by range: 4. It should be granular enough to ensure an even distribution of data.

Chunks

1. a contiguous range of data from a particular collection. 2. Once a chunk has reached about 200M size, the chunk splits into two new chunks. When a

particular shard has excess data, chunks will then migrate to other shards in the system. 3. The addition of a new shard will also influence the migration of chunks.

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Sharding a table

Enable sharding on a database db.runCommand({"enablesharding" : "foo"})

Enable sharding on collection. db.runCommand({"shardcollection" : "foo.bar", "key" : {"_id" : 1}})

Show autosharding status

> db.printShardingStatus()--- Sharding Status ---sharding version: { "_id" : 1, "version" : 3 }shards:{ "_id" : "shard0", "host" : "localhost:10000" }{ "_id" : "shard1", "host" : "localhost:10001" }databases:{ "_id" : "admin", "partitioned" : false, "primary" : "config" }{ "_id" : "foo", "partitioned" : false, "primary" : "shard1" }{ "_id" : "x", "partitioned" : false, "primary" : "shard0" }{ "_id" : "test", "partitioned" : true, "primary" : "shard0","sharded" : { "test.foo" : { "key" : { "x" : 1 }, "unique" : false } } }test.foo chunks:{ "x" : { $minKey : 1 } } -->> { "x" : { $maxKey : 1 } } on : shard0{ "t" : 1276636243000, "i" : 1 }

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Query on sharding assume a shard key of { x : 1 }.

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Sharding machine layoutAvoid single failure

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Review

Getting Up to Speed with MongoDB ( document oriented and schema-free )

Developing with MongoDB (find()) Advanced Usage ( Tons of features) Administration ( Easy to

admin,replication,sharding) MISC (BJSON;internal)

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Misc

1. BSON

2. Datafiles layout

3. Memory-Mapped Storage Engine

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Misc1 BSON (Binary JSON)

a lightweight binary format capable of representing any MongoDB document as a string of bytes.

BSON is the format in which documents are saved to disk. When a driver is given a document to insert,

use as a query, and so on, it will encode that document to BSON before sending it to the server.

Goals:Efficiency Traversability Performance

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Datafiles layout & Memory-Mapped Storage Engine

1. The numeric data files for a database will double in size for each new file, up to a maximum file size of 2GB.

2. Preallocates data files to ensure consistent performance

3. Memory-Mapped storage Engine

4. When the server starts up, it memory maps all its data files.5. OS is to manage flushing data to disk and paging data in and out.6. MongoDB cannot control the order that data is written to disk, which

makes it impossible to use a writeahead log to provide single-server durability.

7. 32-bit MongoDB servers are limited to a total of about 2GB of data per mongod. This is because all of the data must be addressable using only 32 bits.

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Q&A

Getting Up to Speed with MongoDB ( key features)


Advanced Usage ( index & Aggregation, GridFS) Administration ( easy admin,replication,sharding) MISC (BSON; Memory-Mapped)

1 mongo db mongodb (from "humongous) is a scalable, high- performance, open source,...

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