has()<\/em> to narrow down the starting points for your traversal.<\/p>\nFor the first question, we can start from the node with the hostname WEBSERVER-1, follow all incoming edges backward, and from these nodes filter out all nodes that do not have the Internet label.<\/p>\n
gremlin> g.V().has('host','WEBSERVER-1').in().hasLabel('Internet').valueMap('host','ip')\r\n\r\n==>[ip:[10.10.35.2],host:[support.acme.com]]\r\n==>[ip:[10.10.35.3],host:[shop.acme.com]]\r\n==>[ip:[10.10.35.1],host:[global.acme.com]]\r\n<\/pre>\nThe results of our gremlin queries are typically dictionaries that map the desired property names to their values. Since we have three internet nodes dependent on WEBSERVER-1, we get three result dictionaries.<\/p>\n
Question 2<\/h4>\n
The second question requires us to follow a path until a certain condition is met. This can be accomplished using the .repeat().until()<\/em> construct. We start again by finding our starting node, in this case, all nodes labeled with Intranet. Here we can use .as()<\/em> to store the results of this step in a variable, so we can access them later to extract the host and ip properties. We then specify which steps should be repeated from each starting node which in this case is a simple out step, following outgoing edges. We do this until we reach a node where the outE traversal step, usually returning outgoing edges, returns false.<\/p>\nThe project<\/em> step then specifies the values we want to display as our result. This is then followed by an equal number of by-<\/em>modulators. The by-modulator<\/em> specifies which values should be used for the preceding step. In the case of the project<\/em> step with four keys, we supply four by-<\/em>modulators. The first two modulators access the properties host and ip from the previously stored starting nodes. Since after the repeat().until()<\/em> steps the current location in the graph is the final hardware node we are interested in, we can simply access its properties host and ip in the other two modulators.<\/p>\ngremlin> g.V().hasLabel('Intranet').as('intranet')\r\n \t.repeat(out()).until(__.not(outE()))\r\n \t.project('i_host','i_ip','h_host','h_ip')\r\n \t .by(select('intranet').values('host'))\r\n .by(select('intranet').values('ip'))\r\n .by('host')\r\n .by('ip')\r\n\r\n==>[intranet_host:events.acme.com,intranet_ip:10.10.35.2,hardware_host:SAN,hardware_ip:10.10.35.14]\r\n==>[intranet_host:intranet.acme.com,intranet_ip:10.10.35.3,hardware_host:SAN,hardware_ip:10.10.35.14]\r\n==>[intranet_host:humanresources.acme.com,intranet_ip:10.10.35.4,hardware_host:SAN,hardware_ip:10.10.35.14]\r\n<\/pre>\nQuestion 3<\/h4>\n
The third question requires some grouping and aggregating, which works a little differently than you might be used to from declarative languages like SQL. Grouping is achieved by the group()<\/em> step, which expects two by<\/em> modulators. The first tells the group<\/em> step which property should be used for grouping, while the second tells the group<\/em> step which value needs to be produced for each group.<\/p>\nSince we want to produce two values per group to answer the third question (the number of nodes per group and a list with all host names), we use the fold()<\/em> step to wrap all elements within a group, so that we can use match()<\/em> to perform multiple queries on them. The first subquery counts all elements within the folded group, while the second subquery first unfolds the group in order to access the host value from each node and fold them into a new list. Finally, we can select<\/em> the values produced by the two subqueries.<\/p>\ngremlin> g.V().group().by('type').by(\r\n \t fold().match(__.as('x').count(local).as('count'),\r\n \t __.as('x').unfold().values('host').fold().as('names')\r\n \t ).select('count','names'))\r\n\r\n==>[DATABASE SERVER:[count:4,names:[CUSTOMER-DB-1,CUSTOMER-DB-2,ERP-DB,DW-DATABASE]],\r\n STORAGE AREA NETWORK:[count:1,names:[SAN]],\r\n DATABASE:[count:1,names:[DATA-WAREHOUSE]],\r\n APPLICATION:[count:10,names:[CRM-APPLICATION,partners.acme.com,ERP-APPLICATION,events.acme.com,intranet.acme.com,global.acme.com,humanresources.acme.com,support.acme.com,shop.acme.com,training.acme.com]],\r\n HARDWARE SERVER:[count:3,names:[HARDWARE-SERVER-1,HARDWARE-SERVER-2,HARDWARE-SERVER-3]],\r\n WEB SERVER:[count:2,names:[WEBSERVER-1,WEBSERVER-2]]]<\/pre>\nThe result is then a nested dictionary, mapping a dictionary with the count and hostname list to each group key.<\/p>\n
<\/span>Cypher<\/span><\/h3>\nCypher is very different from Gremlin, in that it is a purely descriptive query language, while Gremlin is imperative. It was developed for Neo4j, which is one of the most popular graph databases on offer. The core of the language was since open-sourced as openCypher and it\u2019s now supported by several other graph databases.<\/p>\n
Cypher queries work by matching patterns in the graph topology, filtering and joining the matched nodes and edges, and finally returning the required properties from the result.<\/p>\n
Question 1<\/h4>\n
We use the most basic clauses of Cypher to answer the first question. We MATCH<\/em> all pairs of nodes that are labeled with Internet and VirtualMachine that are connected by an edge. In order to filter the matched parts of the graph, we use the WHERE<\/em> clause to only keep virtual machines with the required hostname. From the internet nodes in the result set, we can then RETURN<\/em> the properties host and IP.<\/p>\nMATCH (inet:Internet)-[:DEPENDS_ON]->(vm:VirtualMachine)\r\nWHERE vm.host = 'WEBSERVER-1'\r\nRETURN inet.host as host,\r\n inet.ip as ip_address<\/pre>\nResult:<\/p>\n\n
\n\n\n\t| host<\/th> | ip<\/th>\n<\/tr>\n<\/thead>\n |
\n\n\t| shop.acme.com<\/td> | 10.10.35.3<\/td>\n<\/tr>\n |
\n\t| support.acme.com<\/td> | 10.10.35.2<\/td>\n<\/tr>\n |
\n\t| global.acme.com<\/td> | 10.10.35.1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\nQuestion 2<\/h4>\nIn addition to matching individual edges, Cypher also lets you describe paths of variable length. The modifier *1..<\/em> in our edge description specifies that we are looking for a path of any length from an Intranet node to a Hardware node. After the first MATCH<\/em> clause, the result still contains extra paths, because each of the three intranet nodes depends on two hardware nodes along the same path. We can filter out the paths to all intermediate dependencies by describing another pattern that we do not want to see at a hardware node, namely that the final node should not have any further dependencies. The WITH<\/em> clause allows us to define local variables. Here we access the first and last node of each path after the filter, so we can more easily extract the properties host and ip in the RETURN<\/em> clause.<\/p>\nMATCH p=(intranet:Intranet)-[:DEPENDS_ON*1..]->(hardware:Hardware)\r\nWHERE NOT (hardware)-[:DEPENDS_ON]->()\r\nWITH NODES(p)[0] as intranet_node,\r\n LAST(NODES(p)) as hardware_node\r\nRETURN intranet_node.host as intranet_host,\r\n intranet_node.ip as intranet_ip,\r\n hardware_node.host as hardware_host,\r\n hardware_node.ip as hardware_ip<\/pre>\nResult:<\/p>\n\n \n\n\n\t| intranet_host<\/th> | intranet_ip<\/th> | hardware_host<\/th> | hardware_ip<\/th>\n<\/tr>\n<\/thead>\n | \n\n\t| events.acme.net<\/td> | 10.10.35.2<\/td> | SAN<\/td> | 10.10.35.14<\/td>\n<\/tr>\n | \n\t| intranet.acme.net<\/td> | 10.10.35.3<\/td> | SAN<\/td> | 10.10.35.14<\/td>\n<\/tr>\n | \n\t| humanresources.acme.net<\/td> | 10.10.35.4<\/td> | SAN<\/td> | 10.10.35.14<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\nQuestion 3<\/h4>\nFor the last question, we can use some of the built-in aggregation functions. We first match all nodes in the graph. When using aggregations in Cypher, every property that is mentioned but not aggregated before the aggregations is implicitly used as a grouping key. Here we group by the type property of the matched nodes, count them, and collect the host names of all nodes.<\/p>\n MATCH (n)\r\nRETURN n.type as type,\r\n count(*) as count,\r\n collect(n.host) as names<\/pre>\nResult:<\/p>\n\n \n\n\n\t| type<\/th> | count<\/th> | names<\/th>\n<\/tr>\n<\/thead>\n | \n\n\t| APPLICATION<\/td> | 10<\/td> | [\"CRM-APPLICATION\", \"ERP-APPLICATION\", \"global.acme.com\", \"support.acme.com\", \"shop.acme.com\", \"training.acme.com\", \"partners.acme.com\", \"events.acme.net\", \"intranet.acme.net\", \"humanresources.acme.net\"]<\/td>\n<\/tr>\n | \n\t| DATABASE<\/td> | 1<\/td> | [\"DATA-WAREHOUSE\"]<\/td>\n<\/tr>\n | \n\t| WEB SERVER<\/td> | 2<\/td> | [\"WEBSERVER-1\", \"WEBSERVER-2\"]<\/td>\n<\/tr>\n | \n\t| DATABASE SERVER<\/td> | 4<\/td> | [\"CUSTOMER-DB-1\", \"CUSTOMER-DB-2\", \"ERP-DB\", \"DW-DATABASE\"]<\/td>\n<\/tr>\n | \n\t| HARDWARE SERVER<\/td> | 3<\/td> | [\"HARDWARE-SERVER-1\", \"HARDWARE-SERVER-2\", \"HARDWARE-SERVER-3\"]<\/td>\n<\/tr>\n | \n\t| STORAGE AREA NETWORK<\/td> | 1<\/td> | [\"SAN\"]<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<\/span>Which Graph Query Language is Best for You?<\/span><\/h3>\nGremlin and Cypher take very different approaches to working with graph data and considering that one is imperative while the other is declarative, it seems like they serve two different crowds.<\/p>\n Gremlin is probably easier to learn for developers with a lot of experience in imperative programming languages. Just like each command in a programming language, each step in Gremlin has a clear effect. Figuring out how these effects work together to traverse a graph and produce an output is akin to writing algorithms over complex data structures.<\/p>\n Cypher feels more like SQL. You describe the result you want to see and the query engine finds a way to extract the required information from your graph. This is probably easier for data scientists and analysts, that are familiar with this approach to working with data.<\/p>\n Your choice might also depend on the graph database you are using. Since Cypher has only recently been adopted by other graph database products like AWS Neptune, some features of the language are not yet fully supported everywhere. And because it is a new offering, some query optimizations you would find in Neo4j, might not happen in AWS Neptune, resulting in generally better performance for Gremlin queries.<\/p>\n <\/span>Conclusions<\/span><\/h2>\nGraph-based approaches allow you to see your data in an entirely new way. Modeling several disconnected datasets in a single graph structure enables you to explore all the connections between the data points.<\/p>\n The basic idea of nodes connected by edges is very versatile and supports many different use cases. This allows you to use a vast range of graph-based algorithms and machine-learning approaches for analyzing your data.<\/p>\n Modern graph databases like Neo4j or AWS Neptune allow you to intuitively query and explore your graph data using graph query languages like Gremlin or Cypher. They also often come with graph algorithms and machine-learning ready to use, so you can get up to speed with your graph in no time.<\/p>\n","protected":false},"excerpt":{"rendered":" When you are working with highly connected datasets, you might find traditional relational databases a little cumbersome to work with. Luckily, specialized databases for all kinds of use cases are available nowadays. Time series databases store time series data, vector databases store vector spaces, and graph databases store highly connected datasets that are best conceptualized […]<\/p>\n","protected":false},"author":315,"featured_media":47257,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"ep_exclude_from_search":false,"footnotes":""},"tags":[1059,385,206,1058,1060],"service":[411,431],"coauthors":[{"id":315,"display_name":"Steven Kutsch","user_nicename":"skutsch"}],"class_list":["post-45690","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","tag-cypher","tag-data-engineering","tag-data-science","tag-graph-databases","tag-gremlin","service-data-engineering","service-data-science"],"acf":[],"yoast_head":"\n Graph Databases and Query Languages in Data Science Use-Cases<\/title>\n<meta name=\"description\" content=\"In this article, we give an introduction to 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