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About DataStax Enterprise Graph

Documentation for developers and administrators on installing, configuring, and using the features and capabilities of DSE Graph.

DataStax Enterprise Graph is a distributed graph database that is optimized for fast data storage and traversals, zero downtime, and analysis of complex, disparate, and related datasets in real time. It is capable of scaling to massive datasets and executing both transactional and analytical workloads. DSE Graph incorporates all of the enterprise-class functionality found in DataStax Enterprise, including advanced security protection, built-in DSE Analytics and DSE Search functionality, visual management and monitoring, and development tools including DataStax Studio.

What is a graph database?

A graph database is a database that uses graph structures to store data along with the data's relationships. Common use cases include: fraud prevention, Customer 360, Internet of Things (IoT) predictive maintenance, and recommendation engine. Graph databases use a data model that is as simple as a whiteboard drawing. Graph databases employ vertices, edges, and properties as described in Data modeling.

What is DSE Graph?

The architecture of the DSE database can handle petabytes of information and thousands of concurrent users and operations per second. DSE Graph is built as a component of DataStax Enterprise. DSE Graph provides the following benefits:

Scalable for large graphs and high volumes of users, events, and operations

DSE Graph can contain hundreds of billions (109) of vertices and edges.

Support for high-volume, concurrent transactions and operational graph processing (OLTP)

The transactional capacity of DSE Graph scales with the size of the cluster and answers complex traversal queries on huge graphs in milliseconds.

Support for global graph analytics and batch graph processing (OLAP)

Available through the Spark framework.

Integration with DSE Search

Integrates with DSE Search for efficient indexing that supports geographical and numeric range search, as well as full-text search for vertices and edges in large graphs.

Native support for Apache TinkerPop and Gremlin query language

Uses the popular property graph data model exposed by Apache TinkerPop and the graph traversal language Gremlin.

Performance tuning options

Numerous graph-level configuration options are available.

Vertex-centric indexes provide optimal querying

Allows optimized deep traversal by reducing search space quickly.

Optimized disk representation

Allows for efficient use of storage and speed of access.

What are the advantages of DSE Graph?

The advantages of DSE Graph over other graph databases include:
  • Integrated with the DSE database to take advantage of the DSE database's features
  • Dedicated index structures that make queries faster
  • Certified for production environments
  • Advanced security features
  • Integrated with Enterprise Search and Analytics
  • Visual management and monitoring with OpsCenter
  • Visual development with DataStax Studio
  • Graph support in certified DataStax drivers

How is DSE Graph different from other graph databases?

DSE Graph is distributed, highly available, and has a scale-out architecture. The data in a DSE Graph is automatically partitioned across all the nodes in a cluster. Additionally, DSE Graph has built-in support for OLAP analytics and search on graph data. All DSE components use advanced security options for sensitive data.

What is Apache TinkerPop?

Apache TinkerPop is an open source project that provides an abstraction framework to interact with DSE Graph and other graph databases.

What is Gremlin?

Gremlin is the primary interface into DSE Graph. Gremlin is a graph traversal language and virtual machine developed by Apache TinkerPop. Gremlin is a functional language that enables Gremlin to naturally support imperative and declarative querying.

How do I interact with DSE Graph?

DataStax recommends using the web-based interactive developer tool DataStax Studio DataStax Studio to create graph schemas, insert data, and query data and metadata. Studio provides both tabulated and visual information for DSE Graph schema and queries, enhancing the exploration of graph relationships.

A more basic way to interact with DSE Graph is the Gremlin console . For production, DataStax supplies a number of drivers for passing Gremlin statements to DSE Graph: Java, Python, Node.js, C#, and C++.

How can I load and unload DSE Graph data?

Use a variety of methods to load or unload data:
  • DSE Graph Loader is a command line utility that supports loading the following formats: CSV, text files, GraphSON, GraphML, Gryo, and queries from JDBC-compatible databases.
  • DataStax Studio and the Gremlin console load data using graph traversals.
  • DseGraphFrame, a framework for the Spark API, loads data to DSE Graph directly or with transformations.

Important: Best practices start with data modeling before inserting data. The paradigm shift between relational and graph databases requires careful analysis of data and data modeling before importing and querying data in a graph database. DSE Graph data modeling provides information and examples.

What tools come with DSE Graph?

DSE Graph comes bundled with a number of tools:

What hardware or cloud environment do I need to run DSE Graph?

DSE Graph runs on commodity hardware with common specifications like other DataStax Enterprise offerings; see DataStax's capacity planning recomendations.

Sours: https://docs.datastax.com/en/dse/5.1/dse-dev/datastax_enterprise/graph/dseGraphAbout.html

DataStax Enterprise Graph

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Technical Guide
DataStax Accelerate Guide for Graph Data Designers and Developers

In our increasingly data-driven world, organizations have more data to manage than ever before. More and more companies are moving to graph databases in order to make sense of the many-to-many relationships of their data. Graph technology enables you to make better and more efficient decisions in real time from the connectedness of your data to create and deliver more intelligent, richer experiences through your modern applications. As the world’s premier Apache Cassandra™ conference, DataStax Accelerate is chock-full of engaging talks and sessions graph database enthusiasts like yourself won’t want to miss.

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Blog
Three Common Mistakes When Building Graph Applications

At DataStax, we help customers build some of the largest production applications on graph databases in the world. From these experiences, we’ve collected a set of three common pitfalls where teams frequently misstep when getting started with graph technology. These themes happen to parallel one of my favorite video games of all time: SimCity 2000.* SimCity 2000 is a game created in the early 1990s that requires a significant amount of trial and error to be successful. As a new player of SimCity 2000, you are naive to the effects of your decisions, and you only learn the consequences through multiple iterations of game play. This blog will draw parallels. Following are the three most common mistakes my graph team sees with building graph applications, plus advice on how to avoid them so that you can save time, skip iterations, and get a head start on building amazing applications using graph data. 1.   Not understanding branching factor To effectively build graph applications, you need to understand what branching factor is and how it affects query runtime. The introduction of graph data into your application brings a new paradigm of data modeling known as “relationship-first design” (as opposed to “entity-first design”). The transition to relationship-first design principles introduces a new set of rules to consider when thinking about your application’s performance. If, like me, you are a fan of SimCity 2000, then this should look familiar: When you start a new game in SimCity 2000, you are introduced to the different game modes and tools available. Just like graph data modeling, the first step to being successful is to examine the options and determine what are the biggest and most important dials you can tweak. Even though there are many tricks to graph data modeling, your graph’s branching factor is the most commonly overlooked dial of your graph’s schema. A graph’s branching factor is the expected or average number of edges that are traversed when you walk from one vertex to another. Unsure what I mean by this? Consider the animation below. In this animation, the goal is to walk the graph until you find your destination. Above, we start at the left-most vertex (the one in green) until we get to the destination vertex on the far right (the one in red). For every vertex you walk (traverse) through from left to the right, you have to explore two more edges to get to the next level. Moving from one vertex to two edges creates two new paths to explore. This causes the number of traversers to double between each level. Each vertex’s branching factor causes the split from one to two traversers during your query. A graph’s branching factor creates exponential growth in the number of traversers required to walk from one vertex to another. The growth in the number of traversers directly correlates to the computational overhead required to process a graph traversal: Frequently, when we troubleshoot slow-running queries, the root cause is a graph data model that creates a higher-than-anticipated branching factor. A high branching factor is one of the main contributing factors to a poor performing Gremlin query. 2. Not planning for or monitoring the growth of supernodes Relationship-first data modeling can create a sleeping time bomb in your graph data—namely, supernodes. A supernode is a node that contains a disproportionately high number of incident edges. Just like in SimCity 2000, high volumes of progress without proper planning will eventually introduce a catastrophe. For the advanced SimCity 2000 player, these catastrophes show up in your game as disasters and monsters. These are unplanned events that decimate your metropolis and bring your city’s advancement to a grinding halt. These time bombs appear as you are traversing your graph, and a bad data model brings your traversal to a grinding halt. These are your supernodes. A supernode is any vertex in your graph that has approximately 100,000 or more edges. You will need to track, mitigate, and eliminate the potential for supernodes within your applications. To find the most likely supernodes in your graph database, you should use your analytics engine to look for the top 10 vertices with the most connections in your graph. You can do this with DataStax via: The results of your query above will let you know if you currently have a supernode in your graph. You should monitor the results of that job periodically to detect and mitigate any potential supernode problems as your graph grows over the lifetime of your application. Monitoring your graph’s health is a great way to reactively handle supernodes in your graph, but the best way to handle them is to be proactive and model your data in a way that mitigates any chance of the supernodes forming. 3. Not fitting the technology to the problem Just like learning the tools and rules for building a successful city in SimCity 2000, you will inevitably make some mistakes. Consider this example below: Naively, I was trying to connect the city in the upper right to the open land in the lower right and lower left. If you brute-force your infrastructure at this point in the game, you don’t end up building what you intended. Instead of connected land, I built broken roads and disconnected bridges. From this I learned, a bit too late, that the ground leveling tool is the place to start before hammering roads over my map. When playing SimCity 2000, it is really easy to spot your errors—they look like broken roads or bridges to nowhere. When starting to integrate graph technology into your stack, it is also inevitable that you will make mistakes. But unlike in SimCity 2000, there are no broken roads or bridges to easily locate these mistakes. I’ve found that people often try to use graph to solve non-graph problems, and typically when my team dives in to troubleshoot a customer’s graph cluster, these are the three most common red herrings that we encounter: Indexes on every property on every vertex Comparing properties to match vertices Putting a graph database behind a BI dashboard Having search indexes on every property of a vertex is an indication that the end use of the application is more about searching data than leveraging the relationships within the data. This type of usage pattern lends itself to using a dedicated search technology, such as DSE Search, which is better optimized to handle these types of questions. The first step in saving your data in a graph is figuring out what determines a unique person, place, or thing within your data. Commonly, teams do not determine this before they load their raw data. Then, in their Gremlin query, they are left with trying to decide which vertices represent the same unique item. This is detrimental to your queries due to the additional computational overhead required and resulting branching factor explosion that it creates. Instead of determining this uniqueness each time you run a query, we recommend using DSE Analytics to match and merge your data before you load it into DSE Graph. Lastly, we know that insights and metrics drive the business decisions that you make on a daily basis. If the end goal of your application is to create a BI dashboard for tracking these metrics, there are a myriad of tools and technologies out there that are well-suited for business intelligence or business analytics. It isn’t very often that a graph database is the right tool for serving up global insights into your company’s analytics. What I mean is: the data from a graph might feed into that insight, and a graph is rarely the best way to serve out the business dashboard. If you have one, please let me know. I am still looking for a good example in this area. Where to go for more graph database insights The seasoned graph experts at DataStax help our customers create graph models that mitigate this risk. If you are looking for more on supernodes, we recommend this great talk by Jonathan Lacefield, our Sr. Director of Product Management. DataStax continues to lead the charge with the most innovative enterprises in building production applications backed by distributed technologies. To accelerate innovation, DataStax formed their Graph Practice, a team of experts focused on growing, advocating, and enabling our customers and the graph industry. As the practice grows, keep watching for our posts, videos, and content on the lessons we learn from the frontlines. We want to hear from and collaborate with you on your graph problem and interesting uses of graph technology. Reach out to us through this blog, check out our contributions to DataStax Academy, or come find us when we are at an event near you. White Paper: Why Graph? READ NOW

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Sours: https://www.datastax.com/products/datastax-graph
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About DataStax Graph

Documentation for developers and administrators on installing, configuring, and using the features and capabilities of DataStax Graph.

DataStax Graph (DSG) is a real-time distributed graph database, tightly integrated in DataStax's distribution of Apache Cassandra®. It is optimized for fast data storage and traversals, zero downtime, and analysis of complex, disparate, and related datasets. DataStax Graph is the graph model for Cassandra that can scale to massive datasets and executing both transactional and analytical workloads. DataStax Graph incorporates all of the enterprise-class functionality found in DataStax Enterprise, including advanced security protection, built-in DSE Analytics and DSE Search functionality, visual management and monitoring, and development tools including DataStax Studio.

What is a graph database?

A graph database uses graph structures to store data along with the data's relationships. Common use cases include fraud prevention, Customer 360, Internet of Things (IoT) predictive maintenance, and recommendation engine. Graph databases use a data model that is as simple as a whiteboard drawing with vertices, edges, and properties as described in Data modeling.

What is DataStax Graph?

The DSE database architecture can handle petabytes of information and thousands of concurrent users and operations per second. DSG has been redesigned, embedded in Cassandra, giving application developers more versatility working with Cassandra and DSE. The following benefits are all featured in DSG:

Scalable for large graphs and high volumes of users, events, and operations

DSG can contain billions (109) of vertices and edges. It takes advantage of the unique scalability of Apache Cassandra(R) to store graph data.

Support for high-volume, concurrent transactions and operational graph processing (OLTP)

The transactional capacity of DSG scales with the size of the cluster and answers complex traversal queries on huge graphs in milliseconds.

Support for global graph analytics and batch graph processing (OLAP)

Available through the Apache Spark (TM) framework.

Deep integration with Cassandra

DSG has been re-engineered to deeply integrate with Cassandra, giving developers the flexibility to read and write graph data using CQL, Gremlin, or both, if desired. Graph data is written once and can be read with either API.

Integration with DSE Search

Integrates with DSE Search for efficient indexing that supports geographical and numeric range search, as well as full-text search for vertices and edges in large graphs.

Native support for Apache TinkerPop and Gremlin query language

Uses the popular property graph data model exposed by Apache TinkerPop and the graph traversal language Gremlin.

Automatic performance tuning

More graph-level configuration is automatically tuned to decrease operational complexity.

Vertex-centric indexes provide optimal querying

Allows optimized deep traversal by quickly reducing search space.

Optimized disk representation

Allows for efficient use of storage and speed of access.

What are the advantages of DataStax Graph?

The advantages of DSG over other graph databases include:
  • Deeply integrated to take advantage of Cassandra's database features, to write data with CQL and read with Gremlin, or vice-versa
  • Dedicated index structures that make queries faster
  • Certified for production environments
  • Advanced security features of DataStax Enterprise
  • Integrated with Enterprise Search and Analytics
  • Visual management and monitoring with OpsCenter
  • Visual development with DataStax Studio
  • Graph support in certified DataStax drivers

How is DataStax Graph different from other graph databases?

DSG is distributed, highly available, and has a scale-out architecture. The data in DSG is automatically partitioned across all the nodes in a cluster like other Cassandra data. Additionally, DSG has built-in support for OLAP analytics and search of graph data. The advanced security options of DataStax Enterprise apply to all graph data.

With the redesign of DSG, three equally capable methods of data model schema creation now exist. Which method you use depends on your skillset and preferences. The methods are:
  • Use Gremlin exclusively to create a graph and schema and query the graph.
  • Use Cassandra Query Language (CQL) to create a graph and schema that can be queried with Gremlin.
  • Convert data stored in Cassandra to a graph that can be queried with Gremlin.

What is Apache TinkerPop?

Apache TinkerPop is an open source project that provides an abstraction framework to interact with DSG and other graph databases.

What is Gremlin?

Gremlin is the primary interface into DSG. Gremlin is a graph traversal language and virtual machine developed by Apache TinkerPop. Gremlin is a functional language that enables Gremlin to naturally support imperative and declarative querying.

How do I interact with DataStax Graph?

DataStax recommends using the web-based interactive developer tool DataStax Studio to create graph schemas, insert query, and query data and metadata. Studio provides both tabulated and visual information for DSG schema and queries, enhancing the exploration of graph relationships.

A more basic way to interact with DSG is the Gremlin console. For production, DataStax supplies a number of drivers for passing Gremlin statements to DSG: Java, Python, Node.js, and C#, C++.

How can I load and unload DataStax Graph data?

Use a variety of methods to load or unload data:
  • DataStax Bulk Loader is a command line utility that supports loading CSV and JSON data files.
  • DataStax Studio and the Gremlin console load data using graph traversals.
  • DseGraphFrame, a framework for the Spark API, loads data to DSG directly or with transformations.

Important: Best practices start with data modeling before inserting data. The paradigm shift between relational and graph databases requires careful analysis of data and data modeling before importing and querying data in a graph database. DSG data modeling provides information and examples.

What tools come with DataStax Graph?

DSG comes bundled with a number of tools:

What hardware or cloud environment do I need to run DataStax Graph?

DSG runs on commodity hardware with common specifications like other DataStax Enterprise offerings; see DataStax's capacity planning recomendations.

Sours: https://docs.datastax.com/en/dse/6.8/dse-admin/datastax_enterprise/graph/dseGraphAbout.html
Graph Data and Code with Denise \u0026 David (Episode 3) - DataStax

DataStax Graph and Graph Analytics

DataStax Graph allows you to perform OLAP queries using Spark.

Many local graph traversals can be executed in real time at high transactional loads. When the density of the graph is too high or the branching factor too large (the number of connected nodes at each level of the graph), the memory and computation requirements to answer OLTP queries go beyond what is acceptable under typical application workloads. These type of queries are called .

are queries that touch either an entire graph or large parts of the graph. They typically traverse a large number of vertices and edges. For example, a query on a social network graph that searches for friends of friends is a scan query.

For applications that use deep and scan queries, using a OLAP query will result in better performance.

Performing OLAP queries using DataStax Graph

Every graph created in DataStax Graph has an OLAP traversal source that is available to gremlin-console and DataStax Studio. This traversal source uses the SparkGraphComputer to analyze queries and execute them against the underlying DSE Analytics nodes. The nodes must be started with Graph and Spark enabled to access the OLAP traversal source. You must connect to the Spark Master node for the datacenter by either running the console from the Spark Master or specifying the Spark Master in the field of the Gremlin console yaml file. For one-off or single-session OLAP queries, alias to and create the query. For example in the Gremlin console:

:remote config alias g .a g.V().count()

If you are performing multiple queries against different parts of the graph, use to return an OLAP traversal source for each part of the graph. For example, in the Gremlin console:

:remote config alias graph mygraph.a categories = graph.snapshot().vertices('category1', 'category2').create()

Note: Alias the graph to the OLAP source before creating a snapshot.

To create a snapshot, supply all the vertices the snapshot will traverse. For example, the following query touches both the and vertices.

def person = graph.snapshot().vertices('Person', 'Address').create() person.V().hasLabel('Person').out('HAS_ADDRESS').count()

Use the method on the snapshot before you call to set TinkerPop's SparkGraphComputer configuration options. For example, to explicitly set the storage level for the snapshot to :

Setting Spark properties from Gremlin

Spark properties can be set from Gremlin using the graph.configuration.setProperty method on the graph.

:remote config alias g .a

By default, Spark applications will use all available resources on the node, so no other Spark application can run. Limit the application's resources before running OLAP traversals by setting the maximum number of cores and the amount of memory used by the traversal. This is particularly important on servers with very large amounts of cores and memory.

For example this request sets 10 executors with 1 core and 4 GB of memory each:

:remote config alias g example_graph.a ==>g=example_graph.ag.graph.configuration.setProperty("spark.cores.max", 10) g.graph.configuration.setProperty("spark.executor.memory", "4g") g.graph.configuration.setProperty("spark.executor.cores", "1")

The property sets the maximum number of cores used by Spark. Setting this property lower than the total number of cores limits the number of nodes on which the queries will be run. The property sets the amount of memory used for each executor. The property sets the number of cores used for each executor.

Before you configure Spark properties from Gremlin kill the currently-running Spark context from the Spark web UI. This will kill all currently running Gremlin OLAP queries. From the Spark web UI, find the application named Apache TinkerPop's Spark-Gremlin and click next to the Application ID.

OLAP traversals create many intermediate objects during execution. These objects are garbage-collected by the JVM, so we recommend configuring a larger pool of executors each with smaller memory and CPU resources, compared to non-graph Spark jobs which typically perform better with fewer executors with higher memory and CPU resources.

We recommend allocating executors with no more then 8 cores (1 should work in most cases) to reduce garbage collection pauses and improve OLAP traversal performance. The memory available to Spark should be equally spread among the cores. For example, if you have 3 nodes and each has 24 cores and 96 GB dedicated to Spark you have 24 * 3 = 72 cores and 96 GB * 3 = 188 GB memory. To allocate all resources you should request 72 single core executors with 4 GB of memory each:

:remote config alias g example_graph.a ==>g=example_graph.ag.graph.configuration.setProperty("spark.cores.max", 72) g.graph.configuration.setProperty("spark.executor.memory", "4g") g.graph.configuration.setProperty("spark.executor.cores", "1")

Some OLAP queries and most queries use Spark SQL joins for traversals. Spark has a predefined number of partitions to perform merge joins, by default set to 200. This can create huge Spark partitions for very large graphs, which slows query execution.

To reduce the size of single partitions and improve performance increase the number of shuffle partitions by setting the property to a larger number. We recommend the is set to 2-4 times the number of Spark cluster cores. So if you have a 200 core cluster, set to 400 or 800.

:remote config alias g example_graph.a ==>g=example_graph.ag.graph.configuration.setProperty("spark.sql.shuffle.partitions", 500)

When to use analytic OLAP queries

On large graphs, OLAP queries typically perform better for deep queries. However, executing deep queries as part of an OLTP load may make sense if they are rarely performed. For example, on online payment provider will favor OLTP queries to process payments quickly, but may require a deep query if there are indications of fraud in the transaction. While the deep query may take much longer as an OLTP workload, on the whole the performance of the application will be faster than segmenting the application into OLTP and OLAP queries.

Long running and periodic processes like recommendation engines and search engines that analyze an entire graph are the ideal use cases for OLAP queries. However, one-off data analysis operations that involve deep queries or that scan the entire database also can benefit from being run as OLAP queries. See OLTP and OLAP for detailed information on performance differences between OLTP and OLAP queries.

Best practices for deleting large numbers of edges and vertices

When deleting large numbers of edges or vertices from a graph, you may end up getting error messages in subsequent queries due the large number of tombstones left in the database before they are automatically removed.

The log entries for such errors resemble the following:

To avoid these errors, reduce the number of tombstones per request by setting the property to a smaller size than the default of 64 MB. The property sets the approximate size of data the Spark Cassandra Connector will request with each individual CQL query.

The following example shows how to set the property to 1 MB and then to drop all phone number vertices from a graph.

:remote config alias g example_graph.ag.graph.configuration.setProperty("spark.cassandra.input.split.size_in_mb", "1") g.V().hasLabel("PhoneNumber").drop().iterate()

DSE authentication and OLAP queries

If DSE authentication is enabled, the internal user runs the application, not the user who submitted the Graph OLAP query.

Sours: https://docs.datastax.com/en/dse/6.8/dse-dev/datastax_enterprise/graph/graphAnalytics/graphAnalyticsSparkGraphComputer.html

Graph datastax

Getting started with graph databases

Getting started with graph databases.

Graph databases are useful for discovering simple and complex relationships between objects. These things can be people, software, locations, automobiles, or anything else you can think of. Relationships are fundamental to how objects interact with one another and their environment. Graph databases are the perfect representation of the relationships between objects.

Graph databases consist of three elements:
vertex
A vertex is an object, such as a person, location, automobile, recipe, or anything else you can think of as nouns.
edge
An edge defines the relationship between two vertices. A person can create software, or an author can write a book. Think verbs when defining edges.
property
A key-value pair that describes some attribute of either a vertex or an edge. Property key is used to describe the key in the key-value pair. All properties are global in DSE Graph, meaning that a property can be used for any vertices. For example, "name" can be used for all vertices in a graph.
Vertices, edges and properties can have properties; for this reason, DSE Graph is classified as a property graph. The properties for elements are an important element of storing and querying information in a property graph.

Property graphs are typically quite large, although the nature of querying the graph varies depending on whether the graph has large numbers of vertices, edges, or both vertices and edges. To get started with graph database concepts, a toy graph is used for simplicity. The example used here explores the world of food.

Elements are labeled to distinguish the type of vertices and edges in a graph database. A vertex that will hold information about a person is labeled person. An edge in the graph is labeled authored. Labels specify the types of vertices and edges that make up the graph. Specifying appropriate labels is an important step in graph data modeling.

Vertices and edges generally have properties. For instance, an person vertex can have a name. Gender and current job are examples of additional properties for a author vertex. Edges also have properties. A created edge can have a createDate property that identifies when the adjoining recipe vertex was created.

Properties can also have properties. Consider the locations that an author may have lived in while authoring books. While knowing the writing location may be interesting by itself, generally an inquirer is interested in the dates that a person lived in a particular location. Would it be interesting to know if Julia Child lived in France or the United States while writing her first cookbook? It could be relevant if the cookbook is on French cuisine.

There are a variety of methods for ingesting data into DSE Graph.
DSE Graph Loader
Data can be loaded using the DSE Graph Loader. CSV, JSON, text parsed with regular expressions, and data selected from a JDBC compliant database can be loaded using a command line tool.
DataStax Studio
Graph API and Traversal API can be used.
Gremlin commands
Data can be added using Gremlin commands. This is a useful method for toy (small graphs) used for development and test. An API exists for adding data using Gremlin commands as well, so Gremlin is common in scripts. The Quick Start shows some of the common Gremlin commands for creating a graph and running traversals.
Gryo
Data can be loaded using Gryo, a binary format, if the data was previously stored in Titan or TinkerGraph. Gryo files can be transferred directly using the schema from the original database.
GraphSON
Data can be entered with GraphSON, a JSON format that is useful for transferring human-readable data. GraphSON files can lose data type information in transfer unless lossless data is generated.
GraphML
Data can be entered using GraphML, an XML format that is useful for transferring graph data. However, data type information is lost with GraphML data transfer.

After loading data, graph traversals are executed to retrieve filtered information. In relational databases, queries are retrieved that combine and filter information. In graph databases, the vertex properties, edge connections, and edge properties all play a role in picking a starting point in the graph and traversing the connections to provide a particular answer to a query. Several , that supply a traversal strategy and traversal engine to use in executing traversals, can be generated for any . Queries in graph databases can consist of several traversals if a complex question is asked, or trivially include no traversals, if a mathematical calculation like 1 + 1 is submitted.

Sours: https://docs.datastax.com/en/dse/6.0/dse-dev/datastax_enterprise/graph/using/GSGraphDBs.html
DS330.03 Property Graph Data Model - DataStax Enterprise 6 Graph

DataStax Graph

Documentation for developers and administrators on installing, configuring, and using the features and capabilities of DataStax Graph (DSG).

DataStax Graph (DSG) is a real-time distributed graph database, tightly integrated in DataStax's distribution of Apache Cassandra®. It is optimized for fast data storage and traversals, zero downtime, and analysis of complex, disparate, and related datasets. DataStax Graph is the graph model for Cassandra that can scale to massive datasets and executing both transactional and analytical workloads. DataStax Graph incorporates all of the enterprise-class functionality found in DataStax Enterprise, including advanced security protection, built-in DSE Analytics and DSE Search functionality, visual management and monitoring, and development tools including DataStax Studio.

Sours: https://docs.datastax.com/en/dse/6.8/dse-dev/datastax_enterprise/graph/graphTOC.html

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