The document discusses schema migrations in NoSQL databases. It begins by defining database schemas and how they differ between SQL and NoSQL databases. In NoSQL databases, schemas are often dynamic or non-existent. The document then examines various techniques for handling schema changes in NoSQL databases including incremental migrations, handling multiple schemas during a transition period, and changing aggregate structures. It provides examples of schema migrations for each technique using MongoDB and graph databases.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
Casandra is a open-source, distributed, highly scalable and fault-tolerant database. It is a best choice for managing structured, semi-structured or unstructured data at a large amount.
This presentation is all about for the difference in between the Sql and NoSQL database because this question generally comes in the mind of every people that on what parameters and
how we can differentiate both these databases.
So, after viewing this presentation all your doubts and misconfusion between Sql and NoSQL got clear.
MongoDB is the most famous and loved NoSQL database. It has many features that are easy to handle when compared to conventional RDBMS. These slides contain the basics of MongoDB.
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
Casandra is a open-source, distributed, highly scalable and fault-tolerant database. It is a best choice for managing structured, semi-structured or unstructured data at a large amount.
This presentation is all about for the difference in between the Sql and NoSQL database because this question generally comes in the mind of every people that on what parameters and
how we can differentiate both these databases.
So, after viewing this presentation all your doubts and misconfusion between Sql and NoSQL got clear.
MongoDB is the most famous and loved NoSQL database. It has many features that are easy to handle when compared to conventional RDBMS. These slides contain the basics of MongoDB.
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
NoSQL Databases: An Introduction and Comparison between Dynamo, MongoDB and C...Vivek Adithya Mohankumar
The research paper covers the consolidated interpretation of NoSQL systems, on the basis of performance, scalability and data aggregation, and compares the types of NoSQL databases based on their implementation and maintenance.
Here is my seminar presentation on No-SQL Databases. it includes all the types of nosql databases, merits & demerits of nosql databases, examples of nosql databases etc.
For seminar report of NoSQL Databases please contact me: ndc@live.in
EVALUATING CASSANDRA, MONGO DB LIKE NOSQL DATASETS USING HADOOP STREAMINGijiert bestjournal
An unstructured data poses challenges to storing da ta. Experts estimate that 80 to 90 percent of the d ata in any organization is unstructured. And the amount of uns tructured data in enterprises is growing significan tly� often many times faster than structured databases are gro wing. As structured data is existing in table forma t i,e having proper scheme but unstructured data is schema less database So it�s directly signifying the importance of NoSQL storage Model and Map Reduce platform. For processi ng unstructured data,where in existing it is given to Cassandra dataset. Here in present system along wit h Cassandra dataset,Mongo DB is to be implemented. As Mongo DB provide flexible data model and large amou nt of options for querying unstructured data. Where as Cassandra model their data in such a way as to mini mize the total number of queries through more caref ul planning and renormalizations. It offers basic secondary ind exes but for the best performance it�s recommended to model our data as to use them infrequently. So to process
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
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Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
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Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
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Schema migrations in no sql
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Prepared By. Dr. Dipali Meher
Schema Migrations
in NOSQL
Dr. Dipali Meher
MCS, M.Phil, NET, Ph.D
Assistant Professor
Modern College of Arts, Science and Commerce,
Ganeshkhind Pune 16
mailtomeher@gmail.com/dipalimeher@moderncollegegk.org
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Prepared By. Dr. Dipali Meher
Agenda
What are database schema?
Schema changes
Schema changes in RDBMS
Schema change sin NOSQL Data
Store
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⊷ The database schema of a database system is its structure
described in a formal language supported by the database
management system (DBMS).
⊷ The term "schema" refers to the organization of data as a
blueprint of how the database is constructed (divided into
database tables in the case of relational databases).
⊷ The formal definition of a database schema is a set of
formulas (sentences) called integrity constraints imposed
on a database..
⊷ The database schema. This describes how real-world
entities are modeled in the database.
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Database Schema
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In NOSQL….
⊷ Recent trends in NOSQL is now schemaless nature – This
allows developers to concentrate on domain design without
worrying about schema changes.
⊷ In case of agility- schema changes are important
⊷ Discussion with domain experts and product owners are
important to understand the data. Discussions should not
contains only the complexity of database schema.
⊷ In case of NOSQL schema changes will be done with least
amount of friction, improving developers productivity and
requires careful attention.
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Schema Changes in RDBMS
⊷ While developing with standard RDBMS technologies, we develop objects, their
corresponding tables, and their relationships.
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Schema Changes in a NoSQL Data Store
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Schema Changes in NOSQL Data Store
Migrations in
Graph
Databases
Changing
Aggregate
Structure
Incremental
Migration
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Schema Changes in NOSQL Data Store
⊷ An RDBMS database has to be changed
before the application is changed. This is
what the schemafree, or schemaless,
approach tries to avoid, aiming at flexibility
of schema changes per entity.
⊷ Frequent changes to the schema are needed
to react to frequent market changes and
product Innovations.
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⊷ When developing with NoSQL databases, in some cases the
schema does not have to be thought about beforehand.
We still have to design and think about other aspects, such as
⊶ Types of relationships (with graph databases),
⊶ Names of the column families, rows, columns,
⊶ Order of columns (with column databases)
⊶ How are the keys assigned and what is the structure of the
data inside the value object (with key-value stores).
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⊷ It is seen that it is applications responsibility to take care of
database schema and data is accepted through application
which is to be saved at database.( e.g. verification and
validation by an application while accepting data)
⊷ When data is not parsed to database by application then
database may throw an error which is to be encountered by an
application. So while designing schemaless databases,
applications and data which is to be stored both are taking
into consideration.
⊷ Schema changes especially matter when there is a deployed
application and existing production data.
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Example of Mongodb
⊷ Consider data model as customer, order, orderItems as follows..
{ "_id": "
4BD8AE97C47016442AF4A580",
"customerid": 99999,
"name": "Foo Sushi Inc",
"since": "12/12/2012",
"order": {
"orderid": "4821-UXWE-122012","orderdate": "12/12/2001",
"orderItems": [{"product": "Fortune Cookies",
"price": 19.99}]
}
}
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Application code to write this document
structure to MongoDB:
BasicDBObject orderItem = new
BasicDBObject();
orderItem.put("product", productName);
orderItem.put("price", price);
orderItems.add(orderItem);
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Code to read the document back from the database:
BasicDBObject item = (BasicDBObject)
orderItem;
String productName =
item.getString("product");
Double price = item.getDouble("price");
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⊷ Changing the objects to add preferredShippingType does not require
any change in the database, as the database does not care that
different documents do not follow the same schema.
⊷ This allows for faster development and easy deployments. All that
needs to be deployed is the application—no changes on the database
side are needed.
⊷ The code has to make sure that documents that do not have the
preferredShippingType attribute can still be parsed
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Example of schema changes
⊷ introducing discountedPrice and renaming price to
fullPrice. To make this change,
⊷ we rename the price attribute to fullPrice and add
discountedPrice attribute.
⊷ The changed document is
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{ "_id": "
5BD8AE97C47016442AF4A580",
"customerid": 66778,
"name": "India House",
"since": "12/12/2012",
"order": {
"orderid": "4821-UXWE-222012",
"orderdate": "12/12/2001",
"orderItems": [{"product": "Chair Covers",
"fullPrice": 29.99,
"discountedPrice":26.99}]
}
}
16
Once we deploy this change, new customers and their orders can be saved and read back without
problems, but for existing orders the price of their product cannot be read, because now the code is
looking for fullPrice but the document has only price.
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Incremental Migration
⊷ Schema mismatch gives new conversion to NOSQL world.
⊷ When schema is changed on the application, we have to make sure to
convert all the existing data to the new schema (depending on data size,
this might be an expensive operation).
⊷ Another option would be to make sure that data, before the schema
changed, can still be parsed by the new code, and when it’s saved, it is
saved back in the new schema.
⊷ This technique, known as incremental migration, will migrate data over
time;
⊷ some data may never get migrated, because it was never accessed.
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Incremental Migration
The incremental migration technique can also be
implemented with a schema_version field on
the data, used by the application to choose the
correct code to parse the data into the objects.
When saving, the data is migrated to the latest
version and the schema_version is updated to
reflect that.
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Incremental Migration
⊷ A proper translation layer between your domain and
the database is important so that, as the schema
changes, managing multiple versions of the schema is
restricted to the translation layer and does not leak into
the whole application.
⊷ Mobile apps create special requirements. Since we
cannot enforce the latest upgrades of the application,
the application should be able to handle almost all
versions of the schema.
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Example Incremental Migration
Read price and fullPrice from document
BasicDBObject item = (BasicDBObject) orderItem;
String productName = item.getString("product");
Double fullPrice = item.getDouble("price");
if (fullPrice == null) {
fullPrice = item.getDouble("fullPrice");
}
Double discountedPrice =
item.getDouble("discountedPrice");
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When writing the document back, the old attribute
price is not saved:
BasicDBObject orderItem = new
BasicDBObject();
orderItem.put("product", productName);
orderItem.put("fullPrice", price);
orderItem.put("discountedPrice",
discountedPrice);
orderItems.add(orderItem);
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When using incremental migration, there
could be many versions of the object on
the application side that can translate the
old schema to the new schema; while
saving the object back, it is saved using
the new object. This gradual migration
of the data helps the application evolve
faster.
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The incremental migration technique will
complicate the object design, especially as
new changes are being introduced yet old
changes are not being taken out.
This period between the change deployment
and the last object in the database migrating
to the new schema is known as the transition
period
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Migrations in Graph Databases
⊷ Graph databases have edges that have types and properties.
If you change the type of these edges in the codebase, you
no longer can traverse the database, rendering it unusable.
⊷ To get around this, you can traverse all the edges and change
the type of each edge.
⊷ This operation can be expensive and requires you to write
code to migrate all the edges in the database.
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Migrations in Graph Databases
⊷ If we need to maintain backward compatibility or do not
want to change the whole graph in one go, we can just
create new edges between the nodes; later when we are
comfortable about the change, the old edges can be
dropped.
⊷ We can use traversals with multiple edge types to traverse
the graph using the new and old edge types.
⊷ This technique may help a great deal with large databases,
especially if we want to maintain high availability.
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Migrations in Graph Databases
⊷ If we have to change properties on all the nodes or edges, we
have to fetch all the nodes and change all the properties that
need to be changed.
⊷ An example would be adding NodeCreatedBy and
NodeCreatedOn to all existing nodes to track the changes
being made to each node.
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Migrations in Graph Databases
for (Node node : database.getAllNodes())
{
node.setProperty("NodeCreatedBy", getSystemUser());
node.setProperty("NodeCreatedOn",
getSystemTimeStamp());
}
⊷ We may have to change the data in the nodes. New data
may be derived from the existing node data, or it could
be imported from some other source.
⊷ The migration can be done by fetching all nodes using
an index provided by the source of data and writing
relevant data to each node.
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Changing Aggregate Structure
⊷ Sometimes you need to change the schema design.
⊷ Example by splitting large objects into smaller ones that are stored
independently.
⊷ Suppose you have a customer aggregate that contains all the customers orders,
and you want to separate the customer and each of their orders into different
aggregate units.
⊷ You then have to ensure that the code can work with both versions of the
aggregates. If it does not find the old objects, it will look for the new
aggregates.
⊷ Code that runs in the background can read one aggregate at a time, make the
necessary change, and save the data back into different aggregates.
⊷ The advantage of operating on one aggregate at a time is that this way, you’re
not affecting data availability for the application.
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Questions to be asked
⊷ Write a short note on Graph Databases.
⊷ What is transition period?
⊷ What is incremental migration?
⊷ Explain incremental migration with and example.
⊷ Write a short note on incremental migration.
⊷ How schema changes will take place in NOSQL data
store? Explain with an example.
⊷ How Schema changes will be done in RDBMS?
Explain with an example.
31