Fundamentals for Python and MongoDB - PDF. an exceptional and modern way of eBook Review: MongoDB with Python and Ming |. (O'Reilly) or, for a Python-specific introduction, MongoDB and Python Using such a schema, Ming will lazily migrate documents as they are. MongoDB has a native Python driver, PyMongo, and a team of Driver PyCon this year on Python and MongoDB that will cover MongoDB, PyMongo, and Ming.
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eBook Review: MongoDB with Python and Ming. August 8, Books, Python Book Review, Print Friendly, PDF & Email. Facebook Twitter Google+ Share. MongoDB and Python: Key Ingredients for a Perfect Big Data Recipe. 1. Open- source is the . appropriate plan for storing data like the Ming framework, queries . MongoDB and Python - pdf - Free IT eBooks Download This week I bought Rick Copeland's MongoDB with Python and Ming eBook from site. It just.
The next generation systems demand horizontal scaling by distributing data over autonomously addable nodes to a running system. For schema flexibility, they also want to process and store different data formats along the sequence factor in the data. NoSQL approaches are solutions to these, hence big data solutions are vital nowadays. But in monitoring scenarios sensors transmit the data continuously over certain intervals of time and temporal factor is the main property of the data. Therefore the key research aspect is to investigate schema flexibility and temporal data integration aspects together.
This problem is evident when extensive data processing is required to find hidden useful information in huge data volumes; but such data mining techniques are not in our current focus [ 14 , 15 , 16 , 17 ], as we limit our discussion to NoSQL temporal modeling and schema based data integration.
Above discussed problems of prolific, multi-structured heterogeneous data in flow urge the researchers to conduct research to find alternate data management mechanisms, hence NoSQL data management systems have appeared and are now becoming a standard to cope with big data problems [ 11 , 18 ].
Such new data management systems are being used by many companies, such as Google, site etc.
The four primary categories of their data model are: i key-value stores, ii column-oriented, iii document, and iv graph databases [ 19 , 20 ]. For rationality, sanity and demonstrating the storage structure, the researchers follow the database schema techniques without losing the advantages of schema flexibility provided by NoSQL databases. Such schema modeling strategies in NoSQL databases are quite different from the relational databases. Collections, normalization and document embedding are few variants to consider during building schema models because they affect the performance and storage effectively because such databases grow very quickly.
While dealing with real-time data, in continuous or sliced snapshot data streams, the data items possess observations which are ordered over time [ 21 ]. During previous years, research efforts had been conducted to capture temporal aspects in the form of data models and query languages [ 22 , 23 ].
But mostly those efforts were for relational or object-oriented models [ 23 ], and can be considered as a conceptual background to solve advanced data management challenges [ 24 ].
The emerging applications, such as sensor data [ 25 ], Internet traffic [ 26 ], financial tickers [ 27 , 28 ] and e-commerce [ 29 ], produce large volumes of timestamped data continuously in real-time [ 30 , 31 ]. The current methods of centralized or distributed storage with static data impose constraints in addressing the real-time requirements [ 32 ], as they inflict pre-defined time convictions unless timestamped attributes are explicitly added [ 31 ]. They have limited features to support the latest data stream challenges and demand research to augment the existing technologies [ 31 , 33 ].
In remote healthcare long term monitoring operations, based on Body Area Networks BAN , demand low energy consumption due to limited memory, processing and battery resources [ 34 ].
These systems also demand communication and data interoperability among sensor devices [ 35 ].
Device interoperability, low energy and miniaturisation features allow the building of large ecosystems, hence enable millions of vendor devices to get integrated and interoperated. IoT ecosystems want general storage mechanisms having structural flexibility to accept different data formats arriving from millions of sensory objects [ 37 ].
The non-relational or NoSQL databases are schema-free [ 2 ]; and allow storage of different data formats without prior structural declarations [ 34 , 37 ]. However for the storage we need to investigate the NoSQL models to design and develop [ 8 , 22 ]; besides flexibly preserving the big data timestamped characteristics for the massive real-time data flow during acquisition processes [ 24 ].
Although all NoSQL databases have unique advantages, but document-oriented storage, as MongoDB provides, is considered robust for handling multiple structural information to support IoT goals [ 38 ]. This rejects the relational structural storage and favours Java Script Object Notations JSON documents to support dynamic schemas; hence provide integration to different data types besides scalability features [ 39 , 40 ].
The authors develop a prototype for the MongoDB NoSQL real-time platform and discuss the temporal data modeling challenges and decisions. An algorithm is presented which integrates JSON data as hierarchical documents and evolves the proposed schema without loosing flexibility and scalability. This article is organized as follows. It is followed by a subsection discussing MongoDB as a well-known document oriented database. This follows a middleware description explaining how to store data in the MongoDB.
Time series in medical data A time series is a sequence of numerical measurements from observations collected at regular durations of time. Such successive times can either be continuous or discrete time periods. Such sequence of values represent the history of an operational context and is helpful in a number of use cases where history or order is required during the analysis.
This sequences of data flows in streams of different speeds and also needs proper management. Data stream and data stream management systems DSMS Data streams, as continuous and ordered flow of incoming data records, are common in wired or wireless sensor network based monitoring applications [ 31 ]. This is not feasible using the traditional DBMS to load the entire data and operate upon it [ 41 ].
Golab et al. Data models and queries must support order and time based operations. Summarized information is stored, owing to the inability of entire stream storage. Performance and storage constraints do not allow backtracking over a stream. Real-time monitoring applications must react to outlier data values. Shared execution of many continuous queries is needed to ensure scalability. First they do not directly store the data persistently rather keep the data in the main memory for some time for autonomous predictions to respond to outlier values, such as fire alarm, emergency situations as in healthcare domain etc [ 42 ].
Therefore DSMS computation is generally data driven, i. In such cases the computation logic always resides in the main memory in the form of rules or queries. On the other hand DBMS approach is query driven, i. Because of data driven nature, the very first issue which DSMS must solve is to manage the changes in data arrival rate during a specific query lifetime. Second, it is not possible to keep all the previous streams in the memory due to their unbounded and massive nature.
Therefore only a summary or synopsis is kept in the memory to answer the queries whereas the rest of the data is discarded [ 21 ]. Third, since we cannot control the order of the data arrival, critical to consider the order of the arrived data values, hence their temporal attribute is essential.
In order to handle the unboundedness of the data, the fundamental mechanism used is that of window - which is used to define slices upon the continuous data to allow correct data flow in finite time [ 42 ].
Data-driven computation, unbounded streams and timestamped data are the main issues that have arisen while dealing with streaming data, such as during sensor data acquisition in monitoring scenarios. This poses novel research challenges and exciting directions to follow with focus on temporal model, techniques and algorithms. These issues need proper management for any of the relational, object-relational or big data management research paradigms; and aim at data modeling and successfully exploiting the time-dependent characteristics for these paradigms ranging from the temporal based models to query models.
Although the directions, developed in previous years for the relational or object-relational domains, provide the basic fundamental footsteps to follow; but require further insights to tackle the advanced Big Data challenges [ 31 , 41 ].
In particular the emerging real-time data-driven applications, having volumes of various data velocities, demand such research inputs to bring number to advantages to the Information and Communication Technology ICT world, specially in promoting IoT and Web 2.
Hence it is becoming mandatory to tackle the challenges associated with temporal data streams for which the relational database management systems have given in. Limitations of RDBMS This section explains what traditional relational approaches lack, why they are not best fit for managing time-variant, dynamically large and flowing data.
This absence has opened the door for a disruptive technology to enter into the market and to gain widespread adoption in the form of NoSQL databases, as it offers better, efficient, cheaper, flexible and scalable solutions [ 43 ].
MongoDB doesn't allow us to specify zero twice. For example, specify tags to 0 below will generate an error. When we specify a field with the value 0, all other fields get the value 1. The default order is ascending.
We use 1 to signify ascending and -1 to signify descending. The first parameter taken by this function is a query object defining the document to be updated. If the method finds more than one document, it will only update the first one. Let's update the name of the author in the article written by Derrick.
In our query below we'll limit the result to one record. The first parameter for this method is the query object of the document we want to delete. If this method finds more than one document, it deletes only the first one found. Let's delete the article with the id 5ba4cbe42e8cace.
Passing an empty query object will delete all the documents. In MongoDB, we can delete a collection using the drop method. I would recommend that the reader visits the official documentation of PyMongo and MongoDB to learn more.
MongoEngine is a library that provides a high-level abstraction on top of PyMongo. Run the command below to install it using pip. After we have imported mongoengine, we use the connect function and specify the database, port, and the host in order to establish a connection with the MongoDB instance.
This means that we need a users and a comments document.