Looking at the new Azure cosmos database, I'm a bit confused about the multi-model nature of it. Specifically, does it mean:
a) That the same underlying database/store can be queried multiple ways concurrently so that I can use both gremlin graph queries and mongodb api against the same collections.
or -
b) Does it mean that you can choose a different model (graph, key value, column, document) at the time of provisioning your Cosmos DB and that is how the data will be stored from then on.
The brochure makes it sound like a), but using the Azure dashboard to create a cosmos instance it makes it seem like b) since you have to choose a model type at creation.
Additionally, the literature makes reference to columnar data, but I don't see the option for it at create time.
Cosmos DB is a single NoSQL data engine, an evolution of Document DB. When you create a container ("database instance") you choose the most relevant API for your use case which optimises the way you interact with the underling data store and how the data is persisted in to that store.
So, depending on the API chosen, it projects the desired model (graph, column, key value or document) on to the underlying store.
You can only use one API against a container, multiple are not possible due to the way the data is stored and retrieved. The API dictates the storage model - graph, key value, column etc, but they all map back on to the same technology under the hood.
Thanks to #Jesse Carter's comment below it appears you are however able to mix and match the graph and DocumentSQL APIs.
From the docs:
Multi-model, multi-API support
Azure Cosmos DB natively supports multiple data models including documents, key-value, graph, and column-family. The core content-model of Cosmos DB’s database engine is based on atom-record-sequence (ARS). Atoms consist of a small set of primitive types like string, bool, and number. Records are structs composed of these types. Sequences are arrays consisting of atoms, records, or sequences.
The database engine can efficiently translate and project different data models onto the ARS-based data model. The core data model of Cosmos DB is natively accessible from dynamically typed programming languages and can be exposed as-is as JSON.
The service also supports popular database APIs for data access and querying. Cosmos DB’s database engine currently supports DocumentDB SQL, MongoDB, Azure Tables (preview), and Gremlin (preview). You can continue to build applications using popular OSS APIs and get all the benefits of a battle-tested and fully managed, globally distributed database service.
Cosmos DB at its heart is a geographically distributed database with its own Atom-Record-Sequence storage engine and index. On top of that infrastructure we are able to implement many different kinds of stores, from SQL like stores using our SQL API, to Mongo, to Cassandra, to Gremlin, to an implementation of Azure Table storage and so on.
Each of the different store types have their own data types (e.g. ways of encoding numbers, dates, etc.) and are encoded in our storage and index layer in their own way. Over time we expect most of those data types to be natively supported by our SQL API. But for now each of our data base types uses its own encoding conventions. When creating an account in Cosmos DB (this is a unit of organization, users can have many accounts) the "type" of Database is specified on the account. So one can have a Table API account or a Mongo account or what have you.
In some cases it is possible to access an account with Data Type X using API Y. For example, one can use SQL API to talk to tables in a Table API account. But outside of graph, that is usually not a great idea. Right now we encode information for each API in a special format and the different data types don't speak each other's formats. So if one were to write to a Table API using SQL API the end result will most likely be corrupt data.
The exception is graph which we work hard to make sure work reasonably well with all database types and we'll have more to say on that in the future.
So if you do want to play around with multi API access we strongly encourage you to only do so in "read only" mode when not using the "native" API for the given account. In other words, by all means play around with the SQL API reading from a Table API, just please don't write to a Table API account suing a SQL API client.
The accepted answer misses out on some points.
Cosmos DB is a NoSQL database, but it is highly distributed and we its storage format is Atom-Record-Sequence.
Why does that matter? We know that it accepts JSON as in- and output formats, that does not mean Cosmos stores its data as JSON, it could be any format actually. This helps us to reason about the multi-modelness of Cosmos: what you get when you execute a query according to a certain model is probably a projection or view of your data.
#JesseCarter already explained we can interchangeably use Document API and Graph API. Last week Table API got publicly announced and probably this API is not too different as well.
The guys over at Spectologic have written a nice blogpost about the Cross-API usage of Cosmos and have also pointed out that the multi-modelness is more cosmetics than internals, the only real exception seems Mongo. The interesting part gets pointed out in the chapter 'Switching the portal experience' here: https://blog.spectologic.com/2017/06/30/digging-into-cosmosdb-storage/
So maybe in the end it boils down to GlobalDocumentDb vs. MongoDb
I too was intrigued by this, wanting to understand more from a API usage auditing perspective and have learned more reading through these answers.
Upon experimenting it appear things have progressed further than the original answers, so to add a contemporary spin...
I have been able to successfully create a Cosmos DB account choosing the SQL API, created a document in the portal then retrieved the document via the MongoDB API.
The original answers suggested that MongoDB was the odd-one-out and couldn't interact with data created with other APIs.
Now whether with fuller testing this would result in corrupt documents due to the data type differences hinted upon by Yaron (https://stackoverflow.com/a/48286729/141022) and whether the storage differences would result in poor performance still as hints to that is to be seen.
For my purposes I'm interested to whether auditing one API is enough, which in this case it is not as data created in one can be retrieved by another, so I haven't tested in depth.
Notably, the ARM template deploys with neither GlobalDocumentDB nor MongoDB kind, however exporting the ARM template back from the portal results in GlobalDocumentDB if that happens to make a difference.
If you are interested in the implementation details of CosmosDB, you can read this whitepaper from a long time ago (assuming that the implementation hasn't changed). http://www.vldb.org/pvldb/vol8/p1668-shukla.pdf
TLDR:
At the bottom, CosmosDB stores data in ARS and exposes them in JSON format.
The database engine index ALL fields in ALL documents by default, therefore enabling very flexible query.
The database engine executes an intermediate language similar to JavaScript, bridging the low-level storage and APIs that database exposes.
Because of that bridging, more database APIs can be added to support different querying mechanism (e.g. SQL, document, columnar).
Multimodel means your data can be stored in a number of different ways. Currently, CosmosDB stores 4 different types of data and it allows you to integrate with an API and build out a user experience around these database storage types.
The 4 types are Document DB or Mongo DB, Graph Database, Key Value Paire, and Wide Column or Column Family.
Related
I have a usecase and needed help with the best available approach.
I use Azure databricks to create data transformations and create table in the presentation layer/gold layer. The underlying data in these tables are in Azure Storage account.
The transformation logic runs twice daily and updates the gold layer tables.
I have several such tables in the gold layer Eg: a table to store Single customer view data.
An external application from a different system needs access to this data i.e. the application would initiate an API call for details regarding a customer and need to send back the response for matching details (customer details) by querying the single customer view table.
Question:
Is databricks SQL API the solution for this?
As it is a spark table, the response will not be quick i assume. Is this correct or is there a better solution for this.
Is databricks designed for such use cases or is a better approach to copy this table (gold layer) in an operational database such as azure sql db after the transformations are done in pyspark via databricks?
What are the cons of this approach? One would be the databricks cluster should be up and running all time i.e. use interactive cluster. Anything else?
It's possible to use Databricks for that, although it heavily dependent on the SLAs - how fast should be response. Answering your questions in order:
There is no standalone API for execution of queries and getting back results (yet). But you can create a thin wrapper using one of the drivers to work with Databricks: Python, Node.js, Go, or JDBC/ODBC.
Response time heavily dependent on the size of the data, and if the data is already cached on the nodes, and other factors (partitioning of the data, data skipping, etc.). Databricks SQL Warehouses are also able to cache results of queries execution so they won't reprocess the data if such query was already executed.
Storing data in operational databases is also one of the approaches that often used by different customers. But it heavily dependent on the size of the data, and other factors - if you have huge gold layer, then SQL databases may also not the best solution from cost/performance perspective.
For such queries it's recommended to use Databricks SQL that is more cost efficient that having always running interactive cluster. Also, on some of the cloud platforms there is already support for serverless Databricks SQL, where the startup time is very short (seconds instead of minutes), so if your queries to gold layer doesn't happen very often, you may have them configured with auto-termination, and pay only when they are used.
Where does Azure Cosmos DB store data? Is it stored in Azure Table or Blob Storage, or something else?
I have looked through the Docs from MS and I do not see this explained anywhere.
I'm sure we can't really answer this question beyond what Microsoft tells us:
Key/value (table), columnar, document, and graph data models are all natively supported because of the ARS (atoms, records, and sequences) design that Azure Cosmos DB is built on. Atoms, records, and sequences can be easily mapped and projected to various data models. The APIs for a subset of models are available right now (SQL, MongoDB, Table, and Gremlin) and others specific to additional data models will be available in the future.
Azure Cosmos DB has a schema agnostic indexing engine capable of automatically indexing all the data it ingests without requiring any schema or secondary indexes from the developer. The engine relies on a set of logical index layouts (inverted, columnar, tree) which decouple the storage layout from the index and query processing subsystems. Cosmos DB also has the ability to support a set of wire protocols and APIs in an extensible manner and translate them efficiently to the core data model (1) and the logical index layouts (2) making it uniquely capable of supporting more than one data model natively.
But the comments to your question are still relevant. If Microsoft does their job right, you shouldn't need to care how or where it's stored. And if you feel that how you proceed building an app is determined on how the data is stored, you probably need to ask a different question. But if you only wanted to know out of curiosity, then here you go.
We have a cloud platform with various Health Care applications. Each application needs what we call reference data. Reference data is always external data coming from a provider on a daily or some regular schedule. An example of reference data is FDB MedKnowledge which includes a comprehensive compendium of consumer medication monographs, along with drug images and imprints.
Various applications will query the reference data to present it to their target customers (who can be physicians, nurses, technicians, procurement department etc...). A common global API will be developed to return the requested data.
Historical information is required ( for ex: FDB in 2017 had NDC1 which then got deleted from the FDB feed in 2019. So a physician who prescribed NDC1 should be able to query the information of that drug going through history).
Daily we receive the feed from the external provider and use it as input source to merge ( update, insert, delete) our reference data copy such that its live table reflects the latest external feed.
In Azure, we have the following storage options:
Blob storage
Cosmos Db
Azure sql database with system versioning
Azure Datawarehouse
Azure Data lake
What is the best practice to store external reference data? We are leaning toward azure sql database with system versioning. Have any of you worked with external reference data? If yes, what is your storage decision and has it worked well for you? I would like to hear your comments and opinions. Thank you!
You need to base your choice on the type of data you are trying to store, and how you need to reference it. It sounds like you might actually need a few different technologies here.
For example, Azure SQL is great for storing relational data. So if your data is tabular in form and needs to have relationships between it, then this is a good choice. However, if you're going to be storing millions and millions of rows then performance might suffer in a relational database. In that sort of scenario, or one where you have lots of transactional data you might want to look at Cosmos DB.
You mentioned images at one point, putting these in a database is not a good idea, in this sort of scenario you are going to want to look at using blob storage.
"Reference Data" really doesn't mean anything, look at the individual types of data you need to store, and how this data is used, and make decisions based on this. For lots of different types of data, there is unlikely to be a one size fits all solution.
I am choosing database technology for my new project. I am wondering what are the key differences between Azure DocumentDB and Azure Table Storage?
It seems that main advantage of DocumentDB is full text search and rich query functionality. If I understand it correctly, I would not need separate search engine library such as Lucene/Elasticsearch.
On the other hand Table Storage is much cheaper.
What are the other differences that could influence my decision?
I consider Azure Search an alternative to Lucene. I used Lucene.net in a worker role and simply the idea of not having to deal with the infrastructure, ingestion, etc.. issues make the Azure Search service very appealing to me.
There is a scenario I approached with Azure storage in which I see DocumentDB
as a perferct fit, and it might explain my point of view.
I used Azure storage to prepare and keep daily summaries of the user activities in my solution outside of Azure SQL Database, as the summaries are requested frequently by a large number of clients with good chances to experience spikes on certain times of the day. A simple write once read many scenario usage pattern (my schema) Azure SQL db found it difficult to cope with while it perfectly fit the capacity of storage (btw daily summaries were not in cache because of size) .
This scenario evolved over time and now I happen to keep more aggregated and ready to use data in those summaries, and updates became more complex.
Keeping these daily summaries in DocumentDB would make the write once part of the scenario more granular, updating only the relevant data in the complex summary, and ease the read part, as the capability of getting parts of more summaries becomes a trivial quest, for example.
I would consider DocumentDB in scenarios in which data is unstructured and rather complex and I need rich query capability (Table storage is lagging on this part).
I would consider Azure Search in scenarios in which a high throughput full-text search is required.
I did not find the quotas/expected perf to precisely compare DocumentDB to Search but I highly suspect Search is the best fit to replace Lucene.
HTH, Davide
I am working with Azure Table storage using the .NET API (TableServiceContext, WCF Data Service, etc). I have a simple graph of objects that I want to save to the table store. In the service context class, I have the following code.
_TableClient.CreateTableIfNotExist("AggRootTable");
this.AddObject("AggRoots", model);
foreach (var related in model.RelatedObjects)
{
this.AddRelatedObject(model, "RelatedCollection", related);
}
this.SaveChanges();
I have used this style of code in WCF Data Services via EF and a SQL Server, but it doesn't work against Azure Tables. I would not expect it to, as there aren't real relationships between tables in Azure. However, the methods are there. Does anyone know how to use AddRelatedObject, AddLink, etc in the context of Azure Tables? Or can suggest approaches to storing object graphs in general? I haven't been able to find any docs, and Google hasn't been helpful.
Thanks,
Erick
You can't. ATS does not support relationships. There are many non-working methods available due to it using data services API.
What you can do, however, is store the full object tree in a single table. Not sure if this will work for your design/architecture
also, it is a bad idea to keep calling CreateIfNotExists before every write operation. First, you pay extra for transactions that occur for the round-trip, second the call is not instantaneous and will slow down your writes.
just precreate the tables before deployment or during roles start.
The Table Storage Service is generally not a good place to store entire object graphs, since there's a size limit (of 1 MB, IIRC) on each row/entity. Obviously, if you know that your object graphs will never be large, you may not care...
A good alternative is often to store a serialized graph in Blob Storage. However, you must have a strategy for how to handle versioning.