In-memory DBMS. TigerGraph also supports larger-than-memory databases under certain circumstances, e.g. when partial or most topology data are on disk.
TigerGraph has its own query language GSQL, a SQL-like graph query language. Documentation can be found here: https://docs.tigergraph.com/dev/gsql-ref
TigerGraph also supports RESTful API to query and update the graph.
As a graph database, TigerGraph has materialized the relationships between data as edges so there is no joining required. Graph analytics focus mainly on how to traverse along the edges.
TigerGraph compresses data in different ways, including dictionary based, snappy, variable byte compression etc. TigerGraph also supports attribute compression. Depending the operation, TigerGraph may not need data decompression before processing.
No. TigerGraph uses MPP (Massively parallel programming) architecture, and runs on commodity servers. The query execution speed is determined by the process power, available memory, network speed and cluster size.