Graph and Cypher in BangDB is quite powerful and allows users to deal with modern and complex use cases. BangDB natively integrates Graph with Stream, which makes in possible to ingest data in stream and keep growing Graph as well. With native AI integration, the data science becomes natural element for Graph. With simple Cypher queries, user can do much more and in real-time for several use cases
Data in a graph table for BangDB is defined as triples. A triple contains subject, object and relationship (predicate) between them. All data is stored as triple within the DB. BangDB does clever arrangements and housekeeping to store the data such that various queries can be written and run efficiently.
The structure of the query is very similar to “Cypher”. BangDB uses Cypher-like queries to process the data. The basic structures look like following
CREATE () -[]-> () – for creating node or triple
S=>() -[]-> () – for querying data
<op USING attr1 SORT_DESC attr2 LIMIT n> query1 ++ query2 – operation on disjoint sets of queries
The ‘()’ denotes subject or object and ‘[]’ denotes relation (predicate) with ‘->’ defining the direction. The arrangement is always “subject Predicate Object”.
The node has a label associated with it. Every node is written as “label:name”.
There are basically following keywords associated with all the queries.
Node, entity creation
CREATE – to create a single node, or triple
Running query and selecting data
S=> – namespace for the unit of query
RETURN – selecting attributes for any query
WHERE – conditions for the query
AS – selecting columns/attributes with alias
DATAQUERY – for filtering within node and relations for properties
SORT_DESC – for sorting in descending order
SORT_ASC – for sorting in ascending order
LIMIT – for limiting number of selections
Statistics
COUNT – counting all using COUNT(*) or COUNT(A.col)
UCOUNT – unique counting
AVG – average of any attribute
MIN – min value
MAX – max value
STD – standard deviation
SUM – sum
EXKURT – ex-kurtosis
SKEW – skewness
Functional properties
SYMM – symmetric relations
ASYMM – asymmetric relations
Graph algos
ALL_PATH – all paths between any two given nodes
SHORT_PATH – shortest path between any two given nodes
Set operations
ADD – adding two or more sets ( UNION )
CROSS – cross product of two sets ( INTERSECT )
SUBTRACT – difference of two sets ( DIFFERENCE )
PIPE – for piping (or sending) the first list to the second query
Data Science
SIMILARITY – compute similarities among set of nodes based on various data
CLUSTER – to find and natural clusters
CENTRALITY – finding the node centrality
COMMUNITY_DETECTION – for detecting several communities within graph
GROUPS – finding several groups given properties
ML_ALGO – this brings entire ML algorithms to the Graph, model name is supplied as well
Deep Learning* – DNN, RNN, ResNet. Embeddable within graph
Information Extraction* – Ontologies or triple generation through IE
Data is processed from left to right. There could be several triples chained to form a query, like.
S1=>() -[]-> () -[]-> () …
Here in the above example, the first triple will intermediate-output a set of results, these intermediate-output will become input from subsequent processing etc. Therefore, it will keep evaluating from left to right using the intermediate results. The subject for subsequent chained query will be the intermediate result of the previous triple and so on.
In some cases, we would like to keep subject of the first triple as subject for the subsequent triple, then we can use the structure like following. This in contrast with the chain query, where object of the first triple becomes the subject of the second one and so on.
S2=>[S1=>() -[]-> ()] -[]-> () …
We will see the examples for these in subsequent sections
We will use BangDB CLI to perform these exercises. But before we go there, let’s see how BangDB Cypher is different from the original Cypher
Checkout a sample use cases here to learn bit more about Graph and Cypher in BangDB
Checkout the graph document here
