I am evaluating databases for my new project where existing data is stored in cosmos SQL database.
For our use case graphDB seem to be a good solution. My Options are Gremlin API or TigerGraph.
I heard Gremlin API is built on top of document database so queries will be slower as Graph queries first gets converted to nosql queries, is that true statement? Any pointers here?
The Gremlin query language is part of Apache TinkerPop. TinkerPop does not dictate how a back end store and query engine be built. It ships with a reference in-memory graph written in Java that actually uses a simple HashMap to store data. TinkerPop has been ported to many different back end stores and storage models. It is not very common however for that to be a document store and there is no need to convert Gremlin queries to SQL unless perhaps you port TinkerPop on top of a Relational or other store and implement it that way. Most Graph DBs I have worked with that implement support for Apache TinkerPop use custom built graph engines and have their own query optimizers that are nothing to do with SQL. I should add that I am not familiar with how the CosmosDB Gremlin support is implemented. The main point is that TinkerPop does not dictate the type of store that be used or how the Gremlin support is implemented.
I am trying to write a record with a different schema to an existing collection with records . I don't get a exception, but i don't see the new record.
Do I need to use a different collection?
DocumentDBRepository<ScheduleViewModel>.CreateItemAsync(task).GetAwaiter();
Cosmos DB doesn't care about what you put into to it (as almost any other nosql db), so this is supported from the Cosmos DB perspective. from the code perspective, I suppose you need to create a connection that would support the model your are using and create a document
https://learn.microsoft.com/en-us/azure/search/search-howto-index-cosmosdb
This page states that you would need to use REST (REST API version 2019-05-06-Preview) to accomplish adding data from Cosmos DB when using the Mongo DB API. Where can I find an updated walk through of this? Or has anyone successfully done this?
Around halfway down that page there's a section called Use REST APIs. This section is an up-to-date walkthrough on how to use the REST API to index data in Cosmos DB. Since you're using the MongoDB API for Cosmos DB, make sure when creating the data source to add ApiKind=MongoDb to the connection string.
I'm a newbie at cosmos db. and i'll be glad if someone help me to find how to catch queries that are going on local emulator. the reason why i'm asking is that now query string is appended in a few steps via linq and i need to be sure that query builder returns correct query string.
thanks in advance!
By the sound of it you are using the C# SDK and LINQ to query your database.
Once your LINQ query is ready you should be doing queryable.AsDocumentQuery() to generate a DocumentQuery object and the use that to do while(documentQuery.HasMoreResults) documentQuery.ExecuteNextAsync<yourtype>().
If that's not the case then you are using the SDK in an non optimal way.
All you need to do to get the generated SQL query is to do a .ToString() on the documentQuery object and you gonna get the translated query back.
In order to see the interactions between your application and Cosmos DB database, you can use Cosmos DB Profiler tool. It shows queries that are sent to Cosmos DB and stacktrace of relevant code that generated them.
The profiler shows statistics for each operation like duration, response status code, query execution metrics etc and also the request units for each request to a database what allows to optimize query costs.
It also alerts users about common pitfalls when using Cosmos DB and recommends how to resolve them.
Available reports provided by the profiler allow to analyze Cosmos DB usage by an application.
As far as I can work out, CosmoDB has the ability to make Graph queries using the Gremlin query language. Apart from that the pricing, marketing etc. all seem the same. It seems strange that they came up with a new product to add Gremlin when they didn't do the same to add MongoDB support. What are the discernable differences between these two products?
The Azure Cosmos DB team member here.
Azure Cosmos DB started as “Project Florence” in 2010 to address developer pain-points faced by large scale applications inside Microsoft. Observing that the challenges of building globally distributed apps are not a problem unique to Microsoft, in 2015 we made the first generation of this technology available to Azure developers in the form of Azure DocumentDB. Since that time, we’ve added new features and introduced significant new capabilities. Azure Cosmos DB is the result. It is the next big leap in globally distributed, at scale, cloud databases. As a part of this release of Azure Cosmos DB, DocumentDB customers, with their data, are automatically Azure Cosmos DB customers. The transition is seamless and they now have access to the new breakthrough system and capabilities offered by Azure Cosmos DB.
In the evolution of Cosmos DB, we have added significant new capabilities since 2015 (when DocumentDB was made generally available) but only a subset of these capabilities was available in DocumentDB. These capabilities are in the areas of the core database engine as well as, global distribution, elastic scalability and industry-leading, comprehensive SLAs. Specifically, we have evolved the Cosmos DB database engine to be able to efficiently map all popular data models, type systems and APIs to the underlying data model of Cosmos DB. The developer facing manifestation of this work currently will experience it via support for Gremlin and Table Storage APIs. And this is just the beginning… We will be adding other popular APIs and newer data models over time with more advances towards performance and storage at global scale.
We also have extended the foundation for global and elastic scalability of throughput and storage. One of the very first manifestations of it is the RU/m (https://learn.microsoft.com/en-us/azure/cosmos-db/request-units-per-minute) but we have more capabilities that we will be announcing in these areas. The new capabilities will help save cost for our customers for various workloads. We have made several foundational enhancements to the global distribution subsystem. One of the many developer facing manifestations of this work is the consistent prefix consistency model (making in total 5 well-defined consistency models). However, there are many more interesting capabilities we will release as they mature.
It is important to point out that we view Azure Cosmos DB as a constantly evolving database service. Typically, we first validate all new capabilities with the large scale applications inside Microsoft, subsequently expose them to key external customers, and finally, release them to the world.
It is also important to point out that DocumentDB’s SQL dialect has always been just one of the many APIs that the underlying Cosmos DB was capable of supporting. As a developer using a fully managed service like Cosmos DB, the only interface to the service is the APIs exposed by the service. To that end, nothing really changes for a DocumentDB customer. Cosmos DB offers the exactly the same SQL API that DocumentDB did. However, now (and in the future) you can get access to other capabilities which were previously not accessible.
DocumentDB is one of the APIs for CosmosDB. Others include Table Storage, MongoDB, Gremlin.
Think about CosmosDB as the database platform that handles scaling, throughput, consitency, etc and DocumentDB as one of the types of the databases than run on CosmosDB.
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.
https://learn.microsoft.com/en-us/azure/cosmos-db/introduction
CosmosDB is the new DocumentDB for NoSQL solution.
As Cosmosdb architect Rimma mentioned
The Azure Cosmos DB DocumentDB API or SQL (DocumentDB) API is now
known as Azure Cosmos DB SQL API. You don't need to change anything to
continue running your apps built with DocumentDB/DocumentDB API. The
functionality remains the same. Thanks.
DocumentDB is one of the APIs for CosmosDB.As of now, if you go to Azure portal and try to create an Azure Cosmos DB, you have to select one of the 4 APIs available there:
Gremlin (Graph)
MongoDB
SQL (DocumentDB)
Table (key-value)