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Indhold
Ordbog annoteret
Android.content.SharedPreferences - BSON
cache-hit - collection.find
consistent hashing - cooperating caches
Data Definition Language (DDL) - findOne()
Data Manipulation Language (DML) - Database query
Database Management System - DOM events
findOne() - JSON
Kollektioner - Mobile and embedded DBMS
Mongod - multicast and directory schemes
Network congestion and server swamping - Persistence for client platforms
The Plaxton/Rajaraman algorithm - replication strategy
Shard - Sharding
shared caching machine - SQLite
swamped - views of witch machines are available
Referencer uden annotering
[Alvermann 2011]- [Copeland 2013]
[Dede 2013]- [Imran m.fl.2003+]
[Karger 1997]- [Nori 2007]
[Padmanabhan 2008]- [Yimeng 2012]
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Noter
TODO: Revisit MongoDb documentation.
Tying the concept of a page to documents in MongoDB, and promoting the use of
semistructured datamodel in a database over calling the database schemaless.
Page …A DBMS stores vast quantities of data, and the data must persist across program
executions. Therefore, data is stored on external storage devices such as disks or tapes,
and fetched into main memory as needed for processing. The unit of information read
from or written to a disk is a page. The size of a page is a DBMS parameter, and typical
values are 4Kb or 8Kb. ... …[Ramakrisnan & Gehrke 2003] p274
I think in the case of a key-value store like MongoDb the page size i increased to Mb.
And further, the page-size and the concept of a document are similar. In a key-value
store collections of document scan be hashed in a distributed system. Further when
accesing a ducument, the whole document is read.
If a document can be compared to page, then the technology con be explained. A
collection is the a dierctory of documents . And the structure of a document itself
forllows the BSON format [Bson xxxx]. Although there is no conceptual model there is
still a format to conform to.. This is call schemaless data-definition. BSON and JSON
[ecma 2013] are both semistructured datamodels [Garcia-Molina m.fl 2012]. The
datamodel is described by the data itself. Attribute-value pairs are distributed
throughout a document, and it is part of search queries to include named attributes in
the resulset of a search. So instead of beeing schemaless i think it is more important to
be semi-structured with limited indexing capabilities.
[Bson xxxx] Homepage BSON,
http://www.bsonspec.org. Creative commons. No-copyright. URL20151021:
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http://www.bsonspec.org
[ecma 2013] ECMA-404.The JSON Data Interchange Format. 1st Edition / October 2013 ECMA International URLPDF20151021: http://www.ecma-international.org/publications/files/ECMA-ST/ECMA-404.pdf Homepage
JSON. Introducing JSON. ECMA-404 The JSON Data Interchange Standard, http://json.org/ URL20151021:
http://json.org/
[Garcia-Molina m.fl 2012] Hector Garcia-Molina, Jeffrey D. Ullman and Jenifer Widom. Database Systems. Selected
Chapters. Compiled by Marcos Vaz Salles. University of Copenhagen. Pearson, 2014. ISBN 978-1-78399-319-2.
[Ramakrisnan & Gehrke 2003] Raghu Ramakrishnan, Johannes Gerke. Database Management Systems. Third Edition.
McGrawHill 2003
Ordbog annoteret
EN: Glossary with citet text. As a learning tool text snippets a copied and referenced.
Android.content.SharedPreferences - BSON
Android.content.SharedPreferences … A developer can use the SDK to retrieve, add,
delete and modify preferences either re- lated to the current activity or shared across
multiple activities for the same user session. For both variants, a public interface is
available, android.con-tent.SharedPreferences that contains two other nested classes,
one as an editor for the current key-value pairs and another which acts as a listener for
changing preferences and related callbacks. …[Fotache 2013] android.os.Environment … lmost the same file and directory related functions are
used, to which dedicated locations as Music, Podcasts, Ringtones, Alarms, Pictures and
others are predefined by the system, since API level 8 (all contained by the public class
Environment - android.os.Environment).
…[Fotache 2013] API Application programming interface. An application programming interface is a
collection of library function in the host language that implements access to an
application. For example JDBC is a database connectivity protocol, and drivers or API
exists to a number of programming languages as libraries or class hierarchies (java, c#,
javascript or php).
Balancer …The balancer (page 28) is a background process that manages chunk
migrations. The balancer can run from any of the query routers in a cluster. When the
distribution of a sharded collection in a cluster is uneven, the balancer process
migrates chunks from the shard that has the largest number of chunks to the shard
with the least number of chunks until the collection balances. For example: if collection
users has 100 chunks on shard 1 and 50 chunks on shard 2, the balancer will migrate
chunks from shard 1 to shard 2 until the collection achieves balance. …[MongoDB
2015D]
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BSON … [bee · sahn], short for Binary JSON, is a binary-encoded serialization of JSONlike documents. Like JSON, BSON supports the embedding of documents and arrays
within other documents and arrays. BSON also contains extensions that allow representation of data types that are not part of the JSON spec. For example, BSON has a Date
type and a BinData type.
BSON can be compared to binary interchange formats, like Protocol Buffers. BSON is
more "schema-less" than Protocol Buffers, which can give it an advantage in flexibility
but also a slight disadvantage in space efficiency (BSON has overhead for field names
within the serialized data).
BSON was designed to have the following three characteristics:
1. Lightweight. Keeping spatial over Database query head to a minimum is important for any data representation format, especially when used over the network.
2. Traversable. BSON is designed to be traversed easily. This is a vital property in
its role as the primary data representation for MongoDB.
3. Efficient. Encoding data to BSON and decoding from BSON can be performed
very quickly in most languages due to the use of C data types.
… [Bson xxxx]
cache-hit - collection.find
cache. …A cache retains a copy of any page it obtains for some time. … [Karger 1997]
cache-hit. Et cache-hit er en request som kan håndteres af cachen. Der laves ikke
opslag på primær serveren(url i request).
cache-miss. Et cache-miss er et cache request som en er forældet, erastttet med et
andet reuest, eller endnu ikke requested. I begge tilfælde må requesten lave opslag /
sende requesten videre til primær serveren(url i request).
Minimize cache memory requirements. …The first is to minimize cache memory
requirements. A protocol should work well without requiring any cache to store a large
number of pages …[Karger 1997]
Cache Resolver. Is a system of caches that uses hashing to determine the primary cache
for each request.
…In this paper we suggest an approach that does away with all inter-cache
communication, yet allows caches to behave together in one coherent system. Cache
Resolver, the distributed web caching system that we developed, eliminates intercache communication on a miss by letting clients decide for themselves which cache
has the required data. Instead of contacting a primary cache that, on a miss, locates the
desired resource in another cache, a user's browser directly contacts the one cache
that should contain the required resource. Browsers make their decision with help of a
hash function that maps resources (or URLs) to a dynamically changing set of available
caches. … [Karger m.fl. 2015]
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Caching …Caching has been employed to improve the efficiency and reliability of data
delivery over the Internet. A nearby cache can serve a (cached) page quickly even if the
originating server is swamped or the network path to it is congested. While this
argument provides the self-interested user with the motivation to exploit caches, it is
worth noting that using widespread use of caches also engenders a general good: if
requests are intercepted by nearby caches, then fewer go to the source server,
reducing load on the server and network traffic to the benefit of all users. …[Karger
m.fl. 2015]
candidate key ..A key is the minimal set of attributes whose values uniquely identify an
entity in the set. There could be more than candidate one key; if so we designate one of
them as the primary key...[Ramakrisnan & Gehrke 2003]
Cassandra er er globalt distribueret database styresystem, som også implementere en
begrænset relationel model; Cassandra er meget skalerbart; Cassandra er ’durable’,
håndterer multiple server udfald på en daglig basis. [Lakshman & Malik 2010]
Client-side Sharded SQL Server and MongoDB. …For our experiments, we created a SQL
Server implementation (SQL-CS) that uses client-side hashing to determine the home
node/shard for each record by modifying the client-side application that runs the YCSB
benchmark. We implemented this client-side sharding so that we could compare
MongoDB(-AS) with SQL Server in a cluster environment. We also took the client-side
sharding code and implemented it on top of MongoDB. This implementation of client-side
sharding on MongoDB is denoted as MongoDB-CS, allowing us to compare MongoDB-AS
with MongoDB-CS (and SQL-CS).
We note that both SQL-CS and Mongo-CS do not support some of the features that are
supported by Mongo-AS. First, whereas Mongo-AS uses a form of range partitioning to
distribute the records across the shards, the Mongo-CS and SQL-CS implementations both
use hash partitioning. Another difference is that the Mongo-CS implementation does not use
any of the routing (mongos), configuration (config db), and balancer processes that are part
of Mongo-AS. As a result, load balancing cannot happen automatically as in Mongo-AS,
where the auto- sharding mechanism aims to continually balance the load across all the
nodes in the cluster. However, Mongo-CS makes use of the basic “mongod” process, which
is responsible for processing the client’s requests. Finally, Mongo-CS and SQL-CS do not
support automatic failover. We note that these features listed above were not the key subject
of performance testing in the benchmark (YCSB) that we use in this paper.
On the flip side, we also note that SQL Server has many features that are not supported in
MongoDB. For example, MongoDB has a flexible data model that makes it far easier to deal
with schema changes. MongoDB also supports read/write atomic operations on single data
entities, whereas SQL Server provides full ACID semantics and multiple isolation levels.
SQL Server also has better manageability and performance analysis tools (e.g. database
tuning advisor).
… .. [Floratou 2012]
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clustered index …When a file is organized so that the ordering of data records is the
same as or close to the ordering of data entries in some index, se say that the index is
clustered; otherwise the index is an unclustered index...[Ramakrisnan & Gehrke 2003]
collection.find() …An important functionality is the usage of cursors by assigning the
result of a collection.find() call to a DBCursor object. Sequential operations can be
performed using the obtained cursor object. In addition to the operations enumerated
above, some administrative actions can be performed as creating indexes, dropping
them and obtaining a list for the collections in the current database.
,,,[Fotache 2013].
consistent hashing - cooperating caches
consistent hashing …We describe a family of caching protocols for distributed
networks that can be used to decrease or eliminate the occurrence of hot spots in the
network … [Karger 1997]
Consistent hashing … consistent hashing assigns a set of items to buckets so that each
bin receives roughly the same number of items. Unlike standard hashing schemes, a
small change in the bucket set does not induce a total remapping of items to buckets. In
addition, hashing items into slightly different sets of buckets gives only slightly
different assignments of items to buckets. We apply consistent hashing to our tree-ofcaches scheme, and show how this makes the scheme work well even if each client is
aware of only a constant fraction of all the caching machines. In [5] Litwin et al
proposes a hash function that allows buckets to be added one at a time sequentially.
However our hash function allows the buckets to be added in an arbitrary order.
Another scheme that we can improve on is given by Devine [2]. In addition, we believe
that consistent hashing will be useful in other applications (such as quorum systems
[7] [8] or distributed name servers) where multiple machines with different views of
the network must agree on a common storage location for an object without
communication.
[2] Robert Devine. Design and Implementation of DDH: A Distributed Dynamic Hashing
Algorithm. In Proceedings of 4th International Con- ference on Foundations of Data
Organizations and Algorithms, 1993.
[5] Witold Litwin, Marie-Anne Neimat and Donovan A. Schneider. -A Scalable,
Distributed Data Structure. ACM Transactions on
Database Systems, Dec. 1996
[7] M. Naor and A. Wool. The load, capacity, and availability of quorum
systems. In Proceedings of the 35th IEEE Symposium on Foundations
of Computer Science, pages 214-225, November 1994.
[8] D.PelegandA.Wool.Theavailabilityofquorumsystems.Information
and Computation 123(2):210-233, 1995.
[9] Greg Plaxton and Rajmohan Rajaraman. Fast Fault-Tolerant Concurrent Access to Shared Objects. In Proceedings of 37th IEEE Symposium on Foundations of Computer Science, 1996.
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…[Karger 1997]
Config servers. …Config servers store the cluster’s metadata. This data contains a
mapping of the cluster’s data set to the shards. The query router uses this metadata to
target operations to specific shards. Production sharded clusters have exactly 3 config
servers. …[MongoDB 2015D]
cooperating caches …To achieve fault tolerance, scalability, and aggregation of larger
numbers of requests (which improves hit rates) several groups [2,7,5,4] have
proposed using systems of several cooperating caches. These systems all share certain
common properties. Every client selects one primary cache in the system. A request
from the client goes to its primary cache. If the primary cache misses, instead of going
directly to the content server, it tries to locate the requested resource in other
cooperating caches. If it succeeds, a slow fetch of the resource from the content server
is replaced by a fetch of the resource from a (presumably closer) cooperating cache.
Thus, the other cooperating caches serve as a ``second level cache'' to reduce the cost
of misses in the primary cache. … [Karger m.fl. 2015]
Data Definition Language (DDL) - findOne()
Data Definition Language (DDL) [Fotache 2013], Definitionssprog. Oprettelse af
tabeller, view, fremmednøgler, indeks.
Data layer in mobile applications. [Fotache 2013].
Data Manipulation Language (DML) - Database query
Data Manipulation Language (DML) [Fotache 2013]. Forespørgsemssprog mod
databasen – SQL (SELECT, INSERT, UPDATE, DELETE).
Data warehouse . .One popular approach to information integratione is th creation of
data warehouses, where information from many legacy databases is copied
periodically, with the appropriate translation, to a central database. …
[Garcia-Molina m.fl 2012]
Database En database er en samling af data som eksisterer i længere tid. [Garcia-Molina
m.fl 2012]
Databasesystem En database er en samling af data over længere tid som administreres
af et databasesystem. [Garcia-Molina m.fl 2012] Et (relationelt) databasessystem har
følgende egenskaber:
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
Brugere kan oprette ny databaser og specificere tilhørende schemaer (logisk struktur for data), ved
hjælp af et specialiseret data-definitions sprog (DDL).

(relationel)
 Brugere skal kunne forespørge på data (EN:query), og ændre data med et sprog kaldet ’query
language’ or ’data manipulation language’ (DML) (relationel).
 Skal kunne opbevare store mængder data (terrabytes) over længere tid.
 Systemet skal kunne komme op igen i en række fejlsituationer eller hvis systemet tages ned
(durable).
 Kun håndtere forspørgsler og ændringer fra mange brugere samtidigt således at rettelser køres som
havde man hele systemet (Isolation). Det har noget med rettigheder at gøre transaktioner. Atomicity
er det at transaktioner kører som en enhed og der kan rulles tilbage eller committes. (relationel ).
..[Garcia-Molina m.fl 2012]
Database query[Fotache 2013]. Forespørgsel mod databasen – SQL (SELECT, INSERT,
UPDATE, DELETE).
database schema,
Database Management System - DOM events
Database Management System [TODO]
Database service in the cloud. … Some limitations might appear when connecting to a
database service in the cloud. A very important functionality covered by the Java
MongoDB driver is the ability to access the aggregation framework (see section 6). The
wrapper around this functionality provided by the driver is DBCollection.aggregate().
Generic DBObject instances can be used by the programmer to create the pipeline of
operations needed in aggregation processes. Finally, and AggregationOutput object can
be obtained after calling the aggregate method over a certain collection. … [Fotache
2013].
minimize the delay …A second objective is, naturally, to minimize the delay a browser
experiences in obtaining a page.…[Karger 1997]
DOM manipulation methods . .. Javasscript provides a large API for dealing with these
DOM structures, in terms of both parsing and manipulating the document. This is one
of the primary ways to accomplish the smaller, piece-by-piece changes to a web page
that we see in an AJAX application[4]… in [Eernisse 2006]
DOM events. … The other important function of the DOM is that it provides a standard
means for Javascript to attach events to elements on a web page. This makes possible
much richer user interfaces, because it allows you to give users opportunities to
interact with the web-page beyond simple links and form elements.
A great example of this is drag-and-drop functionality, which lets users drag pieces of
the page around the screen, and drop them into place to trigger specific pieces of
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functionality. This kind of feature used to exist only in desktop applications, but now it
works just as well in the browser, thanks to the DOM.. … [Eernisse 2006]
findOne() - JSON
findOne() …The driver also allows the developer to use authentication by username and
password. After the connection sequence is performed, collections (all, or a specific one)
can be re- trieved, documents can be inserted, simple find operations can be performed by
calling findOne() method. .. [Fotache 2013].
Harvest Cache. …Chankhunthod et al. [1] developed the Harvest Cache, a more
scalable approach using a tree of caches. A user obtains a page by asking a nearby leaf
cache. If neither this cache nor its siblings have the page, the request is forwarded to
the cache's parent. If a page is stored by no cache in the tree, the request eventually
reaches the root and is forwarded to the home site of the page.
cache retains a copy of any page it obtains for some time. The advantage of a cache tree
is that a cache receives page requests only from its children (and siblings), ensuring
that not too many requests arrive simultaneously. Thus, many requests for a page in a
short period of time will only cause one request to the home server of the page, and
won' t overload the caches either. A disadvantage, at least in theory, is that the same
tree is used for all pages, meaning that the root receives at least one request for every
distinct page requested of the entire cache tree. This can swamp the root if the number
of distinct page requests grows too large, meaning that this scheme also suffers from
potential scaling problems.
[1] Anawat Chankhunthod, Peter Danzig, Chuck Neerdaels, Michael Schwartz and Kurt
Worrell. A Hierarchical Internet Object Cache. In USENIX Proceedings, 1996.
…[Karger 1997]
Hash based partitioning. …For hash based partitioning, MongoDB computes a hash of a
field’s value, and then uses these hashes to create chunks. With hash based
partitioning, two documents with “close” shard key values are unlikely to be part of the
same chunk. This ensures a more random distribution of a collection in the cluster
…[MongoDB 2015D]
Hive …Hive [3] is an open-source data warehouse built on top of Hadoop [2]. It provides a
structured data model for data that is stored in the Hadoop Distributed Filesystem (HDFS),
and a SQL-like declarative query language called HiveQL. Hive converts HiveQL queries to
a directed acyclic graph of MapReduce jobs, and thus saves the user from having to write
the more complex MapReduce jobs directly.
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Data organization in Hive is similar to that found in relational databases. Starting from a
coarser granularity, data is stored in databases, tables, partitions and buckets. More details
about the data layout in Hive are provided in Section 3.3.2.
Finally, Hive has support for multiple data storage formats including text files, sequence
files, and RCFiles [17]. Users can also create custom storage formats as well as
serializers/deserializers, and plug them into the system.
[2] Hadoop. http://hadoop.apache.org/ [3] Hive. http://hive.apache.org/ [17] Y. He, R. Lee, Y. Huai, Z. Shao, N. Jain, X. Zhang, and Z. Xu. RCFile: A Fast and Spaceefficient Data Placement Structure in MapReduce-based Warehouse Systems. In ICDE, pages 11991208, 2011. … .. [Floratou 2012]
home server of the page. . DA: Inden sidens server tilgåes. Sidens server.
Hotspot Et hotspot på nettet er når et stort antal forespøgsler går mod samme system. Hvis et
stort antal klienter samtidig tilgår et website opstår der et hotspot. For at håndtere et stigende
antal request kan request-ene fordeles på et antal servere.
The “hot spot problem”. …The “hot spot problem” is to satisfy all browser page
requests while ensuring that with high probability no cache or server is swamped. The
phrase “with high probability” means “with probability at least 1-1/N”, where Nis a
confidence parameter used throughout the paper. …[Karger 1997]
The inbox search problem ... Inbox Search is a feature that enables users to search
through their Facebook Inbox. At Facebook this meant the system was required to
handle a very high write throughput, billions of writes per day, and also scale with the
number of users. Since users are served from data centers that are geographically
distributed, being able to replicate data across data centers was key to keep search
latencies down. Inbox Search was launched in June of 2008 for around 100 million
users and today we are at over 250 million users and Cassandra has kept up the
promise so far. …[Lakshman & Malik 2010]
Information integration …Joining the information contained in many related databases
into a whole…[Garcia-Molina m.fl 2012]
IP Multicast …Malpani et al. [6] work around this problem by making a group of caches
function as one. A user's request for a page is directed to an arbitrary cache. If the page
is stored there, it is returned to the user. Otherwise, the cache forwards the request to
all other caches via a special protocol called “IP Multicast”. If the page is cached
nowhere, the request is forwarded to the home site of the page. The disadvantage of
this technique is that as the number of participating caches grows, even with the use of
multicast, the number of messages between caches can become unmanageable.
[6] RadhikaMalpani,JacobLorchandDavidBerger.MakingWorldWide
Web Caching Servers Cooperate. In Proceedings of World Wide Web
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Conference, 1996.
…
…[Karger 1997]
JSON …JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is
easy for humans to read and write. It is easy for machines to parse and generate. It is
based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd
Edition - December 1999. JSON is a text format that is completely language
independent but uses conventions that are familiar to programmers of the C-family of
languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These
properties make JSON an ideal data-interchange language.
JSON is built on two structures:
 A collection of name/value pairs. In various languages, this is realized as
an object, record, struct, dictionary, hash table, keyed list, or associative array.
 An ordered list of values. In most languages, this is realized as an array, vector,
list, or sequence.
These are universal data structures. Virtually all modern programming languages
support them in one form or another. It makes sense that a data format that is
interchangeable with programming languages also be based on these structures….
[ecma 2013]
Kollektioner - Mobile and embedded DBMS
key ..A key is the minimal set of attributes whose values uniquely identify an entity in
the set. There could be more than candidate one key; if so we designate one of them as
the primary key...[Ramakrisnan & Gehrke 2003]
Kollektioner har dokumenter. Kollektioner er en stak af dokumenter.
Layers of the enterprise Mobile Applications Development Framework [Unhelkar
2010] in [Fotache 2013]. Det er sikkerhedslaget som dækker alle lag konseptuelt, der
er det 6. lag, som ikke synes på en tegning med 5 lag. Mobile teknologistak:
Preæsentationslag (App); Application (Applicationsserver); Middleware binding;
Information/database; Comminications/TCP. I forhold til webteknologi
 Begrænsede resource på en mobile device
 Kan kræve replikering af data til client persistence layer
 Kan kræve synkronisering af data mellem client- og server persistence layer.
“load” property. …Similarly, over all the client views, the number of distinct objects
assigned to a particular cache is small. We call this property “load”. …[Karger 1997]
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Locality (Cashing) Locality. Regardless of caching scheme, user latency is greatly
influenced by the proximity of the cache servers. Our system ensures that users are
always served by the caches in their physically local regions. Our caches are split
among geographical regions and the users are served only from the caches in their
region. We place the knowledge of determining the user's geographical region inside
the JavaScript function in the users' browsers. The JavaScript function is customizable:
when users download it, they are given a choice of regions. The virtual names
generated by the JavaScript function take on the following form:
A456.ProxyCache3.com, where '456' is the hash of the URL and '3' represents the
variable geographical region.
We then split our DNS system into a two-layer hierarchy. The top layer DNS servers
resolve for the part of the name that contains geographical information and direct the
user's DNS resolver to a set of bottom layer DNS servers that correspond to the user's
geographical region. The bottom layer DNS servers are placed physically in a specific
geographical region of caches and they resolve virtual names in terms of IP addresses
of only those caches. [Karger m.fl. 2015]
MANET A Mobile Ad-hoc Network (MANET) is a collection of autonomous wireless
nodes that may move unpredictably, forming a temporary network without any fixed
backbone infrastructure ([Gruenwald, 03], [IETF, 03]) in [Padmanabhan 2008].
Mobile and embedded DBMS. 11 Characteristics by [Nori 2007] in [Fotache 2013]
a. Embeddable in applications – sometimes requiring no administration. b. Small footprint – in order to be down-
loadable in broader range of mobile devices c. Run on mobile devices and operates in conditions of small amount of processor power, RAM and permanent
memory.
d. Componentized DBMS – supports from all DBMS functions only the ones required by the specific
application.
e. Self-managed DBMS, with no hope for the user to bea DBA who can be able to restore a crashed database.
f. In-memory DBMS – requiring special query processing and indexing technics which are optimized for main
memory usage.
g. Portable databases with very simple deployment.
h. No code in the database that protects against viruses and malware.
i. Synchronize with back-end data sources.
j. Remote management – especially in the case of enterprise-wide applications.
k. Custom programming interfaces for specialized data-centric applications.
Mongod - multicast and directory schemes
Mongod er mongodb daemon
MongoDb klassificeres som et document store[]
MongoDB er skemaløs. Man definerer ikke et skema i databasemæssig forstand. Man
definerer en collection. Man navngiver kollectioner enten implecit ver indsættelse at
første stykke information [Alvermann 2011] eller eksplicit, ved oprettelse
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efterfølgende. Kollektionen er en del at objektnavnet, vis metoder benyttes til at tilgå
dokumentet.
Selve dataformatet for et dokument er BSON [Alvermann 2011] [BSON xxxx]. Binær
JSON. JSON er en semistruktureret datamodel. Modellen en selvbeskrivende og data
angives i key-value pairs. BSON format betyder stadig at det er læsbar tekst. Der ikke
dokumentet der er binært, det er repræsentationen. Mongo DB er programmeret i C
[Cho10] und [Bson, her [BSON xxxx]] I [Alvermann 2011]. I MongoDB får hvert
dokument automatisk en primærnøgle [Alvermann 2011]. Primærnøglen kan tilpasses
[Alvermann 2011]. {} er objekter og [..] er arrays -----[ecma 2013].
MongoDB. MongoDB er et eksempel på et NoSQL datastore [Fotache 2013]. Andre gode
navne for et NoSQL datastore er key-value store. Her lagres data ’clustered’ sammen på
et dokument, som for MongoDB’s vedkommende kan distribueres vandret mellem
MongoDB noder(med en hash nøgle – sharding-key) [Alvermann 2011; Chodorow
2011]. På den måde implemeneterer MongoDB en distribueret database.
MongoDB as persistence layer in the cloud. …In order to better illustrate the above
features, a popular service for MongoDB database in the cloud will be used as
persistence layer for a demonstrative Android application. The service is provided by
MongoLab and it offers physical storage in different data centers like Amazon, Joyent
or Microsoft Azure. At the time of writing of this paper, the available storage options
for shared plans are: 0.5, 1, 2 and 4 GB. The corresponding prices are be- tween 10 to
40 USD each month (except the 0.5 GB instance which is free). The customer can also
buy dedicated plans (details and prices varies from one storage provider to another.
For Amazon hosted MongoDB databases in the cloud, available RAM re- sources start
from 1.7 GB and go up to 68 GB, available processors core are between 1 and 8 and the
user can choose one or more dedicated nodes. Default storage for each unit type varies
from 40 to 160 GB and can be easily extended or moved to SSD disks. Just as a plan
example, for a 34 GB of RAM, 2 dedicated nodes, 4 processor cores, 80 GB storage
capacity (on SSD disks) costs about $3000 each month. Of course, the storage capacity
doesn’t come cheap, but includes additional services as MongoDB monitoring service
activated, real-time access to created log files, 24/7 DBA assistance from 10Gen
(creator of MongoDB), replica sets and dedicated virtual machines. …[Fotache 2013].
MongoDB … Mongo is a high performance and very scalable document-oriented
database developed in C++ that stores data in BSON format, a dynamic schema
document structured like JSON…[Truica et.al. 2013]
MongoDB … MongoDB [7] is a popular open-source NoSQL database. Some of its
features are a document-oriented storage layer, indexing in the form of B-trees, autosharding and asynchronous replication of data between servers.
In MongoDB data is stored in collections and each collection contains documents.
Collections and documents are loosely analogous to tables and records, respectively, found
in relational databases. Each document is serialized using BSON. MongoDB does not
require a rigid schema for the documents. Specifically, documents in the same collection can
have different structures.
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Another important feature of MongoDB is its support for auto-sharding. With sharding, data
is partitioned amongst multiple nodes in an order-preserving manner. Sharding is similar to
the horizontal partitioning technique that is used in parallel database systems. This feature
enables horizontal scaling across multiple nodes. When some nodes contain a
disproportionate amount of data compared to the other nodes in the cluster, MongoDB
redistributes the data automatically so that the load is equally distributed across the
nodes/shards.
Finally, MongoDB supports failover via replica sets, which is its mechanism for
implementing asynchronous master/slave replication. A replica set consists of two or more
nodes that are copies of each other. More information about the semantics of replica sets can
be found in [8]. In the following sections, we use the name Mongo-AS (MongoDB with
auto-sharding) when referring to the original MongoDB implementation.
[7] MongoDB. http://www.mongodb.org/ [8] MongoDB – Replica Sets. http://www.mongodb.org/display/DOCS/Replica+Sets … .. [Floratou 2012]
MongoDB driver is thread safe …As stated in the official documentation, the java
MongoDB driver is thread safe and it is rec- ommended to create a single object which
can be accessed by multiple threads. Internal- ly, this object creates a pool of
connections, and for each operation it is able to find an available connection, use it and
release the resources after it is done. The developer can enforce a certain consistent
behaviour (usage of the same socket by the client) by calling the two specially designed
functions, db.requestStart() and db.RequestDone(). …[Fotache 2013].
Mongos. En Mogos er en process som som kører foran dit cluster, som implementerer
en mongod-server for alle som logger sig på – os transparent overfor
clusterimplementering (Transparant betyder at man logger sig på en process kaldet
mongos, som implementerer een server, som er en implementering af en clusterserver
(clusterserver er en server der styrer andre servere) som var det én server) . Under
overfladen sender den requests /meldinger (router meldinger) til de respektive server
og samler response meldinger efterfølgende. [Chodorow 2011]
Mongos instances. See Query Routers.
multicast and directory schemes …An important issue in many caching systems is how
to decide what is cached where at any given time. Solutions have included multicast
queries and directory schemes. … Consistent hashing provides an alternative to
multicast and directory schemes, and has several other advantages in load balancing
and fault tolerance. … [Karger m.fl. 2015]
Network congestion and server swamping - Persistence for client platforms
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Network congestion and server swamping …Two main of these delays and failures are
congested networks and swamped servers. Date travels slowly through congested
networks. Swamped servers (facing more simultaneous requests than their resources
can support) will either refuse to serve certain requests or will serve them very slowly.
Network congestion and server swamping are common because network and server
infrastructure expansions has not kept pace with the tremendous growth in Internet
use. … [Karger m.fl. 2015]
NoSQL. Term som benyttes for ikke relationelle databaser[Wei-ping 2011]. Betyder vel
egentlig at man ikke kan benytte SQL til at tilgå basen. Basen er i den sammenhæng det
man så kalder ’Persistant datastore’, der hvor data lagres permanent. MongoDB anses
for en NoSql database [Fotache 2013].
NoSQL datastore. MongoDb er et eksempel på et NoSQL datastore [Fotache 2013].
Andre gode navne for et NoSQL datastore er key-value store. Her lagres data ’clustered’
sammen på et dokument, som for MongoDB’s vedkommende kan distribueres vandret
mellem MongoDB noder(med en hash nøgle – sharding-key) [Alvermann 2011; Chodorow
2011]. På den måde implemenetere MongoDB en distribueret database.
Not only SQL. .. recently databases known as nosql systems have been increasingly
used for data not well suited for relational detabases [Lawrence 2014]. Streaming and
soicial mediea data for example.
Page …A DBMS stores vast quantities of data, and the data must persist across program
executions. Therefore, data is stored on external storage devices such as disks or tapes,
and fetched into main memory as needed for processing. The unit of information read
from or written to a disk is a page. The size of a page is a DBMS parameter, and typical
values are 4Kb or 8Kb. ... …[Ramakrisnan & Gehrke 2003] p274
I think in the case of a key-value store like MongoDb. The page size i increaset to Mb.
And further the page-size and the concept of a ducument are similar. In a key-value
store collections of ducment scan be hashed in a distributed system. Further when
accesing a ducument, the whole document is read.
…The basic abstraction of data in a DBMS is a collection of records, or a file, and each
file consists of one or more pages. The files and access methods software layer
organizes data carefully to support fast access to desired subsets of records.
Understanding how these records are organied is essential to using a database system
efficiently …[Ramakrisnan & Gehrke 2003] p273.
...each node in this fugure is a physical page, and retrieving a node involves a disk I/O.
… [Ramakrisnan & Gehrke 2003] p280 about tree-based indexing.
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Parallel Data Warehouse (PDW) …SQL Server PDW [6] is a classic shared-nothing
parallel database system from Microsoft that is built on top of SQL Server. PDW consists of
multiple compute nodes, a single control node and other administrative service nodes. Each
compute node is a separate server running SQL Server. The data is horizontally partitioned
across the compute nodes. The control node is responsible for handling the user query and
generating an optimized plan of parallel operations. The control node distributes the parallel
operations to the compute nodes where the actual data resides. A special module running on
each compute node called the Data Movement Service (DMS) is responsible for shuffling
data between compute nodes as necessary to execute relational operations in parallel. When
the compute nodes are finished, the control node handles post-processing and re-integration
of results sets for delivery back to the users.
[6] Microsoft SQL Server 2008 R2 Parallel Data Warehouse.
http://www.microsoft.com/sqlserver/en/us/solutions- technologies/data-warehousing/pdw.aspx … .. [Floratou m.fl. 2012]
Persistent data layer [Fotache m.fl 2013]. Et Persistent datastore er et persistent data
layer. Et lag er en abstract beskrivelse for et teknologilag. Hvilke lag I teknologistakken
er det persistente/faste/durable teknologilag for data. Man kan tale om et server
persistence layer og et client-persistence layer. Client persistence layer kan være til
web, som en cache eller tilsvarende på en mobile device. Many mobile devices require
replication and synchronization with centralized larger servers.
Persistent datastore. En SQL datastore og en NoSQL datastore er eksempler på
persistent datastore. Et persitent datastore bevarer til tilstand hvis systemet tages ned.
(system down). Et Persistent datastore er et persistent data layer.
Persistence for client platforms[Fotache 2013]. Det på klientsiden og her tales apps,
men med ’light-weight-sql-datastores eller med en NoSQL-sikkert-også-light-weigt
repræsentation med ’f.eks MongoDb’ som lagring af semistrukturerede datamodeler i
dokumenter. Formaliseret udnyttelse af en cache i webtermer.
The Plaxton/Rajaraman algorithm - replication strategy
The Plaxton/Rajaraman algorithm. Plaxton and Rajaraman [9] show how to balance
the load among all caches by using randomization and hashing. In partic- ular, they use
a hierarchy of progressively larger sets of “virtual cache sites” for each page and use a
random hash function to as- sign responsibility for each virtual site to an actual cache
in the network. Clients send a request to a random element in each set in the hierarchy.
Caches assigned to a given set copy the page to some members of the next, larger set
when they discover that their load is too heavy. This gives fast responses even for
popular pages, be- cause the largest set that has the page is not overloaded. It also
gives good load balancing, because a machine in a small (thus loaded) set for one page
is likely to be in a large (thus unloaded) set for another. Plaxton and Rajaraman's
technique is also fault tolerant.
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The Plaxton/Rajaraman algorithm has drawbacks, however. For example, since their
algorithm sends a copy of each page request to a random element in every set, the
small sets for a popular page are guaranteed to be swamped. In fact, the algorithm uses
swamp- ing as a feature since swamping is used to trigger replication. This works well
in their model of a synchronous parallel system, where a swamped processor is
assumed to receive a subset of the incom- ing messages, but otherwise continues to
function normally. On the Internet, however, swamping has much more serious
consequences. Swamped machines cannot be relied upon to recover quickly and may
even crash. Moreover, the intentional swamping of large num- bers of random
machines could well be viewed unfavorably by the owners of those machines. The
Plaxton/Rajaraman algorithm also requires that all communications be synchronous
and/or that mes- sages have priorities, and that the set of caches available be fixed and
known to all users.
[9] Greg Plaxton and Rajmohan Rajaraman. Fast Fault-Tolerant Concurrent Access to
Shared Objects. In Proceedings of 37th IEEE Symposium on Foundations of Computer
Science, 1996.
…[Karger 1997]
Primary cache miss. See cooperating caches. Every request goes to a primary cache
server. If the primary cache misses, instead of going directly to the content server, it
tries to locate the requested resource in other cooperating caches.
…The systems (systems of several cooperating caches) differ in precisely how data is
located in the case of a primary cache miss. Some schemes broadcast a query to all
other caches using multicast [7] or UDP broadcasts [2]. Besides consuming excess
bandwidth with broadcast queries, the primary cache must wait for all cooperating
caches to report misses before it contacts the content server; this can slow down
performance on second-level cache misses. Other schemes use directories, either
centralized [5] or repeatedly broadcast to support local queries [4]. Directory queries
or transmissions also consume bandwidth, and centralized directories can become
new points of failure in the system [5]. … [Karger m.fl. 2015]
primary key ..A key is the minimal set of attributes whose values uniquely identify an
entity in the set. There could be more than candidate one key; if so we designate one of
them as the primary key...[Ramakrisnan & Gehrke 2003]
Query Routers (mongos instances). Query Routers, or mongos instances, interface with
client applications and direct operations to the appropriate shard or shards. The query
router processes and targets operations to shards and then returns results to the
clients. A sharded cluster can contain more than one query router to divide the client
request load. A client sends requests to one query router. Most sharded clusters have
many query routers. [MongoDB 2015D]
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random cache trees …Our first tool, random cache trees, … we use a tree of
caches[Chankhood xxx] to coalesce requests. Like Plaxton and Rajaraman, we balance load
by using a different tree for each page and assigning tree nodes to caches via a random hash
function[Placton xxxx]. …we prevent any server from becoming swamped with high
probability, a property not possessed by either Chankhunthod et al. or Plaxton/Rajaraman. In
addition, our protocol shows how to minimize memory requirements (without significantly
increasing cache miss rates) by only caching pages that have been requested a sufficient
number of times. …[Karger 1997]
Range-based sharding. …For range-based sharding, MongoDB divides the data set into
ranges determined by the shard key values to provide range based partitioning.
Consider a numeric shard key: If you visualize a number line that goes from negative
infinity to positive infinity, each value of the shard key falls at some point on that line.
MongoDB partitions this line into smaller, non-overlapping ranges called chunks
where a chunk is range of values from some minimum value to some maximum value.
Given a range based partitioning system, documents with “close” shard key values are
likely to be in the same chunk, and therefore on the same shard. …[MongoDB 2015D]
Ranged hash function. A ranged hash function maps to a bucket. …A ranged hash family
is a family of ranged hash functions. A random ranged hash function is a function drawn at
random from a particular ranged hash family. . …[Karger 1997]
replication strategy to store copies of hot pages throughout the Internet ..Several
approaches to overcoming the hot spots have been proposed. Most use some kind of
replication strategy to store copies of hot pages throughout the Internet; this spreads
the work of serving a hot page across several servers. In one approach, already in wide
use, several clients share a proxy cache. All user requests are forwarded through the
proxy, which tries to keep copies of frequently requested pages. It tries to satisfy
requests with a cached copy; fail- ing this, it forwards the request to the home server.
The dilemma in this scheme is that there is more benefit if more users share the same
cache, but then the cache itself is liable to get swamped. .. …[Karger 1997]
Shard - Sharding
Shard. …Shards store the data. To provide high availability and data consistency, in a
production sharded cluster, each shard is a replica set 1 . For more information on
replica sets, see Replica Sets. …[MongoDB 2015D], [MongoDB 2015C]
Shard key. …MongoDB distributes data, or shards, at the collection level. Sharding
partitions a collection’s data by the shard key. To shard a collection, you need to select
a shard key. A shard key is either an indexed field or an indexed compound field that
exists in every document in the collection. MongoDB divides the shard key values into
chunks and distributes the chunks evenly across the shards. To divide the shard key
values into chunks, MongoDB uses either range based partitioning or hash based
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partitioning. See the Shard Key (page 18) documentation for more information.
…[MongoDB 2015D]
Shard Key. Ved konfiguration kan auto-sharding slås til og så kan collectioner shardes
over flere maskiner. Dokumentnøglen som gives hvert dokument som primærnøgle,
kan ændres og konfigureres efter befor. Shard key er den definerede primærnøgle,
som anvendes til at sharde/fordele de enkelte dokumenter mellem de konfigurerede
maskiner [Alvermann 2011].
Sharded cluster about mongostat: …Discovers and reports on statistics from all
members of a replica set or sharded cluster. When connected to any member of a
replica set, --discover all non-hidden members of the replica set. When connected to
a mongos, mongostat will return data from all shards in the cluster. If a replica set
provides a shard in the sharded cluster, mongostat will report on non-hidden members
of that replica set. …[MongoDB 2015]
Sharding Ifølge [Chodorow 2011] er sharding den partitioneringsmetode Mongo DB
implementerer til at automatisk partitionere data mellem nye maskiner og modsat
samle /coalesce.
Sharding. MongoDB er født med sharding. Slåes auto-sharding til, bliver en database
automatisk fordelt over et antal maskiner. Det kaldes horisontal partitionering. De
enkelt dokumenter kopieres til forskellige maskiner. For at konfigurere sharding til
MongoDB er der 3 komponenter:
 En shard. Er en maskine der holder en delmængde af data.
 En mongos. En slags router der kan route forespørgslerne til de enkelte
shards.Og sammensætte svar til et samlet svar. En konfigurationsserver er
nødvendig for mongos…
 En konfigurationsserver. Her ligger oplysningerne om de enkelte shards.
[Alvermann 2011].
shared caching machine - SQLite
shared caching machine …The most obvious approach, of providing a group of users
with a single, shared caching machine, has several drawbacks. If the caching machine
fails, all users are cut off from the Web. Even while running, a single cache is limited in
the number of users it can serve, and may become a bottleneck during periods of
intense use. Finally, two important limits arise on the hit rate a single cache can
achieve.
 First, since the amount of storage available is limited, the cache will suffer
``false misses'' when requests are repeated for objects which it was forced to
evict for lack of space.
 Second, the limit on the number of users that the cache can serve works against
the desire to aggregate requests from as many users as possible for caching
purposes: typically, the more user requests are aggregated together, the better
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the hit rate becomes as one user requests objects already requested by other
users.
…[Karger m.fl. 2015]
“smoothness” property. When a machine is added to or removed from the set of
caches, the expected fraction of objects that must be moved to a new cache is the
minimum needed to maintain a balanced load across the caches. …[Karger 1997]
Software options. [Fotache 2013]. Teknologier. … Mahmoud et al. [Mahmoud 2012] in
[Fotache 2013] point out the most common software options for storage in mobile
applications:
 HTML5 (localStorage API which stores objects as key-value pairs and IndexDB
which implements relational technology);
 SQLLite – a over-simplified relational database server;  Cloud storage (Apple iCloud, Dropbox, Google Drive, etc.);  Device specific storage (APIs, tools, frameworks such as WebWorks, Shared
Preferences, Network IO, WebView, Core Data. ..
Splitting ..Splitting is a background process that keeps chunks from growing too large.
When a chunk grows beyond a specified chunk size (page 32), MongoDB splits the
chunk in half. Inserts and updates triggers splits. Splits are an efficient meta-data
change. To create splits, MongoDB does not migrate any data or affect the shards. …
[MongoDB 2015D]
“spread” property. …Second, over all the client views, the total number of different
caches to which a object is assigned is small. We call this property “spread”. …[Karger
1997]
SQL datastore. Postgres er et eksempel på et SQL datastore[Fotache 2013]. Et SQL
datastore kaldes for en relationel database. Andre vigtige eksempler på relationelle
databaser er Oracle, DB2 og MySQL. En relationel database implementerer et fortolket
sprog, SQL, som følger den relationelle algebra. I modsætningen til den relationelle
algebra som beskriver en algebra med unikke tupler, kan databaser have dublerede
tubler og SQL implementerer da det som kaldes for ’bag-theory’, at resultatsæt fra SQL
forespørgsler kan have dublerede tupler.
SQLite … Among other data layer options, the most popular SQLite, advertised by its
producers as a software library that implements a self- contained, serverless, zeroconfiguration, transactional SQL database engine. In fact, SQLite is present on a variety
of devices and operation systems, precompiled packages can be downloaded for Linux,
Mac OS, Win- dows, Windows Phone 8, Windows .Net platform [7], [5] [3]. Its
popularity can be ex- plained by the following features [3]:
 easy to install and configure ;
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



simplicity ;
does not require a server (can run in cli
ent-only mode) ;
compactness of database (all database resides in a single file for each
application);
it is an open source product.
…
The small footprint library can handle both DML and DDL statements, of- fers cursors
access to clients, indexes, multi- ple data types, primary and foreign keys and a lot
more features, which makes it very easy to use and a good candidate for being used as
a persistence layer. …[Fotache 2013] .
swamped - views of witch machines are available
.
swamped Many of us have experienced the hot spot phenomenon in the context of the Web.
A Web site can suddenly become extremely popular and receive far more requests in a
relatively short time than it was originally configured to handle. In fact, a site may receive
so many requests that it becomes “swamped,” which typically renders it unusable. Besides
making the one site inaccessible, heavy traffic destined to one location can congest the
network near it, interfering with traffic at nearby sites. …[Karger 1997]
.
tag aware sharding. …MongoDB allows administrators to direct the balancing policy
using tag aware sharding. Administrators create and associate tags with ranges of the
shard key, and then assign those tags to the shards. Then, the balancer migrates tagged
data to the appropriate shards and ensures that the cluster always enforces the
distribution of data that the tags describe. Tags are the primary mechanism to control
the behavior of the balancer and the distribution of chunks in a cluster. Most
commonly, tag aware sharding serves to improve the locality of data for sharded
clusters that span multiple data centers. See Tag Aware Sharding (page 26) for more
information… [MongoDB 2015D]
TCP and UDP port numbers...Originally, port numbers were used by the Network
Control Program (NCP) in the ARPANET for which two ports were required for halfduplex transmission. Later, the Transmission Control Protocol (TCP) and the User
Datagram Protocol (UDP) needed only one port for full-duplex, bidirectional traffic.
The even-numbered ports were not used, and this resulted in some even numbers in
the well-known port number range being unassigned. The Stream Control
Transmission Protocol (SCTP) and the Datagram Congestion Control Protocol (DCCP)
also use port numbers. They usually use port numbers that match the services of the
corresponding TCP or UDP implementation, if they exist.
The Internet Assigned Numbers Authority (IANA) is responsible for maintaining the
official assignments of port numbers for specific uses.[1] However, many unofficial
uses of both well-known and registered port numbers occur in practice... [Imran
m.fl.2003+]
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User Datagram Protocol, UDP …User Datagram Protocol, UDP er en protokol til
overførsel af data. UDP er en del af Internet-protokolstakken, som oftest
benævnes TCP/IP. I protokolstakken anvendes enten TCPeller UDP. UDP giver ingen
garanti for at data kommer frem (eller rettere: Afsenderen får ikke besked hvis data
ikke kommer frem, ligeledes får afsender ikke besked hvorvidt data er modtaget).
UDP tilhører TCP/IP protokol stakkens 4. lag. Derfor er der tilført "pakke-headeren" et
lag yderligere. I forhold til IP er der tilføjet et portnummer. Dette portnummer bruges
til "demultiplexing" (engelsk) af data, for at sørge for, at det rigtige data bliver leveret
til den rigtige process på computeren. Desuden yder UDP en checksum service, der
garanterer at indholdet af en DatagramPacket (pakke) er intakt. Bevæger man sig et
lag længere ned i protokol stakken, møder man netværk-lagets; IP-protokol, IKKE
garanteret levering- eller modtagelse af pakker. …[Rasser m.fl. 2004]
ultrametric …The ultrametric is a natural model of Internet distances, since it
essentially captures the hierarchical nature of the Internet topology, under which, for
example, all machines in a given university are equidistant, but all of them are farther
away from another univer- sity, and still farther from another continent. The logical
point-to- point connectivity is established atop a physical network, and it is generally
the case that the latency between two sites is determined by the “highest level”
physical communication link that must be traversed on the path between them.
Indeed, another definition of an ultrametric is as a hierarchical clustering of points.
The distance in the ultrametric between two points is completely determined by the
smallest cluster containing both of the points. …[Karger 1997]
unclustered index …When a file is organized so that the ordering of data records is the
same as or close to the ordering of data entries in some index, se say that the index is
clustered; otherwise the index is an unclustered index...[Ramakrisnan & Gehrke 2003]
View. …We define a view to be the set of caches of which a particular client is aware. …
A client uses a consistent hash function to map a object to one of the caches in its view.
…[Karger 1997]
views of witch machines are available. …The Internet, however, does not have a fixed
collection of machines. Instead, machines come and go as they crash or are brought
into the network. Even worse, the information about what machines are functional
propagates slowly through the network, so that clients may have incompatible “views”
of which machines are available to replicate data. This makes stan- dard hashing
useless since it relies on clients agreeing on which caches are responsible for serving a
particular page. …[Karger 1997]
Referencer uden annotering
Resultat af en søgning på scholar.google.com på ’MongoDB’ [Lassen 2015].
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[Alvermann 2011]- [Copeland 2013]
[Alvermann 2011] Markus Alvermann. Alles ist möglich. Einführung in MongoDB. JavaSPEKTRUM 1/2011
URL20151005: https://www.iks-gmbh.com/assets/downloads/Einfuehrung-in-MongoDB-iks.pdf. URL:
www.javaspektrum.de
[Boicea 2013] Alexandru Boicea, Florin Radulescu, Laura Ioana Agapin, "MongoDB vs Oracle -- Database
Comparison", EIDWT, 2012, 2013 Fourth International Conference on Emerging Intelligent Data and Web
Technologies, 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies 2012, pp.
330-335, doi:10.1109/EIDWT.2012.32. URL20151005:
http://www.computer.org/csdl/proceedings/eidwt/2012/4734/00/4734a330-abs.html
[Bson xxxx] Homepage BSON,
http://www.bsonspec.org. Creativ commons. No-copyright. URL20151021:
http://www.bsonspec.org
[Chodorow 2011] Kristina Chodorow. Scaling MongoDB. Sharding, Cluster Setup and Administration. O’Reilly 2011.
URL20151005: https://books.google.no/books?hl=da&lr=&id=Fp6NtavDpEC&oi=fnd&pg=PR5&dq=mongodb&ots=iTT8IhDPWl&sig=DiB0xrj8-_jEZ2_Kdx6zzeiUCA&redir_esc=y#v=onepage&q=mongodb&f=false
[Chodorow 2011B] Kristina Chodorow. 50 Tips and Tricks for MongoDB Developers. O’Rielly 2011.
URL20151005: https://books.google.no/books?hl=da&lr=&id=Np_aXDGVKoC&oi=fnd&pg=PR5&dq=mongodb&ots=jNC8QI_3GH&sig=fhSXEojdXSL4plcUIvyfsrrCh2Y&redir_es
c=y#v=onepage&q=mongodb&f=false
[Chodorow 2013] Kristina Chodorow MongoDB: The Definitive Guide. Powerfull and scalable data storage. O’Rielly,
2013. ISBN 978-1-449-34468-9. URL20151005:
https://books.google.no/books?hl=da&lr=&id=uGUKiNkKRJ0C&oi=fnd&pg=PP1&dq=mongodb&ots=h8mxJeeTte&s
ig=vwf6uyPyjWOu8ug2mHen4MRLQ4k&redir_esc=y#v=onepage&q=mongodb&f=false
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