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1 Mongo DB MongoDB (from "humongous) is a scalable, high- performance, open source, document-oriented database. Written in C++. Home:

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1 1 Mongo DB MongoDB (from "humongous) is a scalable, high- performance, open source, document-oriented database. Written in C++. Home: http://www.mongodb.org/http://www.mongodb.org/ Support by http://www.10gen.com/http://www.10gen.com/ Production Deploy http://www.mongodb.org/display/DOCS/Production+ Deployments http://www.mongodb.org/display/DOCS/Production+ Deployments

2 2 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)

3 3 Getting Up to Speed with MongoDB Key Features of MongoDB 1. 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

4 4 Getting Up to Speed with MongoDB 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})

5 5 Getting Up to Speed with MongoDB 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.

6 6 Getting Up to Speed with MongoDB Different from Relation database 1.easy & simple admin/dev Kill –INT to shutdown instance 2.no-transaction 3.no-relation 4.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.

7 7 Getting Up to Speed with MongoDB Different from key-value database 1. 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.

8 8 Agenda Getting Up to Speed with MongoDB ( Summary : document oriented & schema-free ) Developing with MongoDB (start & shutdown & connect & query & DML) Advanced Usage ( index & Aggregation, GridFS) Administration ( admin,replication,sharding) MISC

9 9 Developing with MongoDB Continue. Start MongoDB connect Query DML ( create, insert, update, delete, drop ) Stop cleanly

10 10 Developing with MongoDB 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 4.5 Fri Apr 1 14:37:08 [initandlisten] git version: 9c28b1d608df0ed6ebe791f63682370082da41c0 Fri 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 27017 Fri Apr 1 14:37:08 [websvr] web admin interface listening on port 28017

11 11 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)]

12 12 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})

13 13 Developing with MongoDB 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);... }); adam matt zak

14 14 Developing with MongoDB Behind 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); }

15 15 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"}}) Delete db.users.remove() db.mailing.list.remove({"opt-out" : true}) Drop collection db.foo.drop();

16 16 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()

17 17 Developing with MongoDB 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 disk 4. halt. use the shutdown command > use admin switched to db admin > db.shutdownServer(); server should be down...

18 18 Agenda Getting Up to Speed with MongoDB ( Summary : document oriented & schema-free ) Developing with MongoDB (Summary: find()) Developing with MongoDB (Summary: find()) Advanced Usage ( index & Aggregation, GridFS) Administration ( admin,replication,sharding) MISC

19 19 MongoDB Advanced Usage Advanced Usage Index Aggregation MapReduce Database commands Capped Collections GridFS: Storing Files

20 20 MongoDB Advanced Usage Index MongoDBs 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()

21 21 MongoDB Advanced Usage 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 indexs key and dirX is the indexs 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 }

22 22 MongoDB Advanced Usage 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" ] }

23 23 MongoDB Advanced Usage 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.

24 24 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;... } }}})

25 25 MongoDB Advanced Usage 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};... }

26 26 MongoDB Advanced Usage Database commands Commands implement all of the functionality that doesnt 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()

27 27 MongoDB Advanced Usage Capped Collections 1. 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.

28 28 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 youre already using MongoDB, GridFS obviates the need for a separate file storage architecture. GridFS will leverage any existing replication or autosharding that youve set up for MongoDB, so getting failover and scale-out for file storage is easy. GridFS can alleviate some of the issues that certain filesystems can exhibit when being used to store user uploads. For example, GridFS does not have issues with storing large numbers of files in the same directory. You can get great disk locality with GridFS, because MongoDB allocates data files in 2GB chunks.

29 29 MongoDB Advanced Usage GridFS: example $ echo "Hello, world" > foo.txt $./mongofiles put foo.txt connected to: 127.0.0.1 added file: { _id: ObjectId('4c0d2a6c3052c25545139b88'), filename: "foo.txt", length: 13, chunkSize: 262144, uploadDate: new Date(1275931244818), md5: "a7966bf58e23583c9a5a4059383ff850" } done! $./mongofiles list connected to: 127.0.0.1 foo.txt 13 $ rm foo.txt $./mongofiles get foo.txt connected to: 127.0.0.1 done write to: foo.txt $ cat foo.txt Hello, world

30 30 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 collections fs.chunks fs.files system.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 }

31 31 MongoDB Advanced Usage 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

32 32 DBA on MongonDB Administration ( admin,replication,sharding) Monitoring Security and Authentication Backup and Repair Master-Slave Replication Replication-set Sharding

33 33 DBA on MongonDB Easy Monitoring Using the Admin Interface db.runCommand({"serverStatus" : 1}) mongostat Third-Party Plug-Ins

34 34 Using the Admin Interface

35 35 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 },

36 36 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

37 37 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.

38 38 Backup on MongonDB 1. Data File Cold Backup kill –INT mongod; copy --dbpath 2. mongodump (exp) and mongorestore (imp) 3. fsync and Lock 4. Slave Backup > use admin switched 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" }

39 39 Repair MongonDB 1. Need to repair databases after an unclean shutdown ( kill -9 ) ************** old lock file: /data/db/mongod.lock. probably means unclean shutdown recommend removing file and running --repair see: 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 humongous 4. Repairing a database will also perform a compaction. 5. db.repairDatabase() can repair single database

40 40 DBA on MongonDB Replication Master-Slave Replication Replication-set

41 41 Master-Slave Replication $ 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 read 2.Backup on Slave 3.Process data on Slave 4.DR

42 42 Master-Slave Replication How it works? The Oplog oplog.$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 masters oplog and applying operations in order to stay up-to-date. async

43 43 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.

44 44 Replication on MongonDB Setup Replication-set 1.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: $./mongod --dbpath ~/dbs/node2 --port 10002 --replSet autocomplete/slcdbx1006:10001 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 $./mongod --dbpath ~/dbs/node3 --port 10003 –replSet autocomplete/slcdbx1005:10001, slcdbx1006:10002

45 45 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

46 46 MongonDB Auo-sharding Sharding : splitting data up and storing different portions of the data 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.

47 47 Auto sharding When to shard? 1.Youve 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.DR 5.Failover automatically

48 48 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 }

49 49 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.

50 50 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 }

51 51 Query on sharding assume a shard key of { x : 1 }.

52 52 Sharding machine layout Avoid single failure

53 53 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)

54 54 Misc 1. BSON 2. Datafiles layout 3. Memory-Mapped Storage Engine

55 55 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

56 56 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.

57 57 Q&A Getting Up to Speed with MongoDB ( key features) Developing with MongoDB (start & shutdown & connect & query & DML) Advanced Usage ( index & Aggregation, GridFS) Administration ( easy admin,replication,sharding) MISC (BSON; Memory-Mapped)


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