Lecture If failure occurs, recovery can be handled by

Lecture Notes in Computer Science:
Authors’ Instructions for the Preparation
of Camera-Ready Contributions
to LNCS/LNAI/LNBI Proceedings

Ayush Hofmann1, Pavi
Barth1, Ursala Beyer1, Christine Günther1,

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Frank Holzwarth1, Anna Kramer1, and Erika Siebert-Cole1


1 Springer-Verlag, Computer Science Editorial,
Tiergartenstr. 17,
69121 Heidelberg, Germany

Ingrid.Beyer, Christine.Guenther, Frank.Holzwarth,

Erika.Siebert-Cole, LNCS}@Springer.com

Abstract. Algorithms and applications for extensive systems have generally
assumed a fairly simplistic failure model: The computer is defined as a
reliable digital machine, with consistent execution times and meager amount of failures.

If failure occurs, recovery can be handled by checkpoint-restart. With the push
toward exascale computing, the concern of preserving the reliable, digital
machine model will become too costly has become even greater, and we must focus
towards Resilient programming to improve algorithms. In this Paper we discuss a
Strategy for programming resilience in MongoDB.

Keywords: Resilient, MongoDB,
Idempotent, Algorithm, Queries, Non-Idempotent,


Errors and random
corruptions may affect the result computation of many modern machines. Resilient
algorithms and programming is a strategy that deals with network errors and
outages and command errors. These are algorithms that are designed to perform
in the presence of memory errors.


Mongo DB is a leading   and one
of the most prominent open-source document database and a NoSQL database and
this database is written in C++.

The fact that Mongo DB stores data in JSON-like
documents that can vary in structure and sizes. Related information is stored
together for fast query access through the MongoDB query language. MongoDB uses
dynamic schemas, meaning that you can create records without first defining the
structure, such as the fields or the types of their values. You can change the
structure of records (which we call documents) simply by adding new fields or
deleting existing ones. This data model, helps you with the ability to
represent stratified relationships, to store arrays, and other more complex
structures easily. Documents in a compilation need not have a similar   set of
fields and denormalization of data is quite common. Mongo DB, was also designed
with high availability and scalability in consciousness, and includes
out-of-the-box replication and auto-sharding. Example of insert, find and
update queries in MongoDB.


1.1   Code


  user_id: ‘bcd001’,

  age: 45,

  status: ‘A’






  { age: { $gt: 25 } },

  { $set: { status: ‘C’ } },

  { multi: true }




Literature Survey

section of the paper surveys many research papers that what are the problems
that Mongo DB faces and how resilient data structure can help for it.1
proposes the resilient SDN (Software Defined Network) based architecture for
Industrial Control Networks and it portrays several SDN based fast
technologies.  In 2 a design theory is
developed for resilient software intensive-systems because of which different
communities having different technologies can share a common frame of reference
and knowledge. In 3 Persistency-of-communication (PoC) is been introduced
that deals with the communication failure problem of self triggered consensus
network. 4 Provides jest of resilient software defined networks for
technology related disasters. In 5 focus is mainly on the automatic loading and
balancing of the Mongo DB and proposes heat based load balancing which is very economical.

In 6 modeling style style of Mongo DB or NoSQL is discussed that if
normalization is extended as it has less space for document or collection, then
how it reduces the query execution time without using join operations. 7
Provides the modeling of the Mongo DB with the relational algebra and also
analyses the feasibility of moving from SQL database to NoSQL. It does provide
the optimization of the Mongo DB. 8 Proposes a method which can achieve
robustness to transmission errors of Dirac which is an open source video codec.

9 Proposes an algorithm that detects fault tolerant termination based of
Dijkstra that is previous fault sensitive scheme. 10 Proposes a monitor that
can integrate any Mongo DB deployment with the help of easy configuration
changes results shows the it requires low overhead access. 11 Shows that a
document based on NoSQL (Mongo DB) which uses JSON data and how it explores the
merits of NoSQL Databases.

Proposed Work

This Paper will give you deep understanding on
MongoDB concepts needed to deploy a resilient database.

In order
to develop effective re-salient algorithms and applications, we ?rst need
programming models
that permit us to reason about failure and implement recovery.


We’re going to do Update One resiliently:


          {‘$inc’: {‘counter’: 1}},


The operation counts the number of times an event
occurred, by incrementing a field named “counter” in a document whose
id is today’s date. Pass “upsert=True” to create the document if this
is the first event in the day.


We Identify three errors that could occur while
sending our message to MongoDB.

3.1 Transient Errors

When we send his update One message to Mongo DB,
the driver may see a transient error from the network layer, such as a TCP
reset or a timeout. The driver cannot tell if the server received the message
or not, so we don’t know whether his counter was incremented. There are other
transient errors that look the same as a network blip. If the primary server
goes down, the driver gets a network error the next time it tries to send it a
message. This error is brief, since the replica set elects a new primary in a
couple seconds. Similarly, if the primary server steps down (it’s still
functioning but it has resigned its primary role) it closes all connections.

The next time the driver sends a message to the server it thought was primary,
it gets a network error or a “not master” reply from the server.

Persistent errors

There might also be a lasting network outage.

When the driver first detects this problem it looks like a blip: the driver
sends a message and can’t read the response. Again, we cannot tell whether the
server received the message and incremented the counter or not.


3.2 Command errors

When the driver sends a message, MongoDB might
return a specific error, saying that the command was received but it could not
be executed. Perhaps the command was    misformatted,
the server is out of disk space, or Ian’s application isn’t authorized.

Handling the Errors

The Server Discovery and Monitoring Spec requires
a MongoDB driver to track the state of each server it’s connected to. This data
structure is called the “topology description”. If there’s a network
error while talking to a server, the driver sets that server’s type to
“Unknown”, then throws an exception. The topology description is:

Server 1: Unknown

Server 2: Secondary

Server 3: Secondary

In the case of a command error, what the driver
thinks about the server hasn’t changed: if the server was a primary or a
secondary, it still is. Thus the driver does not change the state of the server
in its topology description, it just throws the exception.


Code that is about resilience tries 5 times or
even 10 times.


i = 0

while True:




    except network error:

        i += 1

        if i == MAX_RETRY_COUNT:



In the case of a network blip, we no longer risks
undercounting. Now we risk over counting, because if the server read our first update
One message before we got a network error, then the second update One message
increments the counter a second time.

During a persistent outage, retrying more than
once wastes time. After the first network error, the driver marks the primary
server “unknown”; when we retry the operation, it blocks while the
driver attempts to reconnect, checking twice per second for 30 seconds. If all
that effort within the driver code hasn’t succeeded, then trying again from his
code, reentering the driver’s retry loop, is fruitless.

3   LNCS

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Notes in Computer Science series have access to all the pdfs of all the online
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4   Results

Idempotent operations are those which have the
same outcome whether you do them once or multiple times. If we make all his
operations idempotent, we can safely retry them without danger of over counting
or any other kind of incorrect data from sending the message twice.


MongoDB has four kinds of operations: find,
insert, delete, and update.




Queries are naturally idempotent.


  doc = findOne()

except network err:

  doc = findOne()


This insert is idempotent. The only warning is,
we have assumed we have no unique index on the collection besides the one on
_id that MongoDB automatically creates.


doc = {_id: ObjectId(),



except network err:



    except DuplicateKeyError:

# first try worked





If we delete one document using a unique value
for the key, then doing it twice is just the same as doing it once.


  deleteOne({‘key’: uniqueValue})

except network err:

  deleteOne({‘key’: uniqueValue})





This Update is Naturally

 updateOne({ ‘_id’: ‘2016-06-28’},
{‘$set’:{‘sunny’: True}}, upsert=True)



Dealing with the original non-idempotent update


updateOne({ ‘_id’:

          {‘$inc’: {‘counter’: 1}},




We’re going to split it into two parts. Each will
be idempotent, and by transforming this into a pair of idempotent operations
we’ll make it safe to retry.


In Part One, we leave N alone, we just add a
token to a “pending” array. we need something unique to go here; an Object
ID does nicely:

oid = ObjectId()


  updateOne({ ‘_id’: ‘2016-06-28’},

            {‘$addToSet’: {‘pending’: oid}},


except network err:

    try again, then throw



For Part Two, with a single message we query for
the document by its _id and its pending token, delete the pending token, and
increment the counter.


  # Search for the document by _id and pending

  updateOne({‘_id’: ‘2016-06-28’,

             ‘pending’: oid},

            {‘$pull’: {‘pending’: oid},

             ‘$inc’: {‘counter’: 1}},


except network err:

    try again, then throw


So we can safely retry this update One. Whether
it’s executed once or twice, the document ends up the same:


  _id: ‘2016-06-28’,

  counter: N + 1,




5   Lecture
Notes in Computer Science in the ISI SCI Expanded

. The
algorithm introduced in this research paper, is a modified programme that
provides a storage effective alternative for Resilient Programming. we
presented the algorithm for using a richard tree.  it does not use repeatitions, which makes it
more effective over a for loop checker. This superiority increases
exponentially as the lenght of the code increases. In this paper we took quries
and converted them into the proposed Resilient Quries. Manually Coding for
Resilience results in a zero Energy Model, which is desirable.


1.  J. Vestin, A. Kassler and J. Akerberg,
“Resilient software defined networking for industrial control
networks,” 2015 10th International Conference on Information,
Communications and Signal Processing (ICICS), Singapore, 2015, pp. 1-5.


2. J. Rajamäki and R. Pirinen,
“Critical Infrastructure Protection: Towards a Design Theory for Resilient
Software-Intensive Systems,” 2015 European Intelligence and Security
Informatics Conference, Manchester, 2015, pp. 184-184.


3. D. Senejohnny, P. Tesi and C. De Persis,
“A Jamming-resilient Algorithm for Self-triggered Network
Coordination,” in IEEE Transactions on Control of Network Systems,
vol. PP, no. 99, pp. 1-1.


4. C. Mas Machuca et al.,
“Technology-related disasters: A survey towards disaster-resilient
Software Defined Networks,” 2016 8th International Workshop on
Resilient Networks Design and Modeling (RNDM), Halmstad, 2016, pp. 35-42.


5. X. Wang, H. Chen and Z. Wang,
“Research on Improvement of Dynamic Load Balancing in
MongoDB,” 2013 IEEE 11th International Conference on Dependable,
Autonomic and Secure Computing, Chengdu, 2013, pp. 124-130.


6. A. Kanade, A. Gopal and S. Kanade,
“A study of normalization and embedding in MongoDB,” 2014 IEEE
International Advance Computing Conference (IACC), Gurgaon, 2014, pp. 416-421.


7. G. Zhao, W. Huang, S. Liang and Y. Tang,
“Modeling MongoDB with Relational Model,” 2013 Fourth
International Conference on Emerging Intelligent Data and Web Technologies,
Xi’an, 2013, pp. 115-121.


8. Myo Tun and W. A. C. Fernando, “An
Error-Resilient Algorithm Based on Partitioning of the Wavelet Transform
Coefficients for a DIRAC Video Codec,” Tenth International Conference
on Information Visualisation (IV’06), London, England, 2006, pp. 615-620.


9. H. Magal, R. Ianconescu and P. Meron,
“A robust error resilient video compression algorithm,” Military
Communications Conference, 1994. MILCOM ’94. Conference Record, 1994 IEEE, Fort
Monmouth, NJ, 1994, pp. 247-251 vol.1.


10. Ten-Hwang Lai and Li-Fen Wu, “An
(N-1)-resilient algorithm for distributed termination detection,”
in IEEE Transactions on Parallel and Distributed Systems, vol. 6, no. 1,
pp. 63-78, Jan 1995.


11. L. Vokorokos, M. Uchnár and A. Baláž,
“MongoDB scheme analysis,” 2017 IEEE 21st International
Conference on Intelligent Engineering Systems (INES), Larnaca, 2017, pp.



12. P. Colombo and E. Ferrari,
“Enhancing MongoDB with Purpose-Based Access Control,” in IEEE
Transactions on Dependable and Secure Computing, vol. 14, no. 6, pp. 591-604,
Nov.-Dec. 1 2017.


13. B. Jose and S. Abraham, “Exploring
the merits of nosql: A study based on mongodb,” 2017 International
Conference on Networks & Advances in Computational Technologies (NetACT),
Thiruvanthapuram, 2017, pp. 266-271.





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