TigerGraph

Challenges most often run into with TigerGraph & GSQL:

1. Steep GSQL learning curve (esp. accumulators & traversal mental models). New teams often struggle with when/how to use vertex/global accumulators and how to reason about side effects. TigerGraph’s own docs and user reviews call out the learning curve explicitly. 

1. Loading & schema-evolution quirks. Users report occasional inconsistencies during bulk loads and friction when evolving schemas; release notes and UI docs also flag upgrade-related issues (e.g., GraphStudio mappings disappearing after v2.4).
    
2. Performance surprises & timeouts. Naïve traversals/self-joins can be slower than expected; repeated executions sometimes hit the 60s query-timeout; cluster clock drift can also cause odd behavior. 

1. Version/Environment gotchas. Small version jumps can change behavior (e.g., code moved from 3.4→3.5 acting differently) and some GraphStudio actions run “interpreted mode,” which isn’t always supported.
    
2. Edge-case limitations in GSQL features. There are documented “known issues” around certain accumulator combinations (e.g., MapAccum with edge accumulators; ArrayAccum placement) and some features aren’t supported in distributed modes; exact float comparisons can also behave unexpectedly. 

1) Neo4j AuraDB (managed Cypher)

• Fastest path: export TigerGraph CSV → import with neo4j-admin.
• Rewrite GSQL to Cypher (WITH, aggregations).
• Managed service, huge community, free tier.
   

2) Amazon Neptune (AWS-native)

• If on AWS, easiest switch.
• Bulk load from S3, query in openCypher/Gremlin.
• Fits fraud/recommendation workloads.
   

3) ArangoDB or Memgraph

• ArangoDB: graph + JSON docs, managed option.
• Memgraph: lightweight, in-memory, openCypher.
• Both: load CSV → rewrite GSQL to AQL or Cypher.Challenges most often run into with TigerGraph & GSQL:

1. Steep GSQL learning curve (esp. accumulators & traversal mental models). New teams often struggle with when/how to use vertex/global accumulators and how to reason about side effects. TigerGraph’s own docs and user reviews call out the learning curve explicitly. 

1. Loading & schema-evolution quirks. Users report occasional inconsistencies during bulk loads and friction when evolving schemas; release notes and UI docs also flag upgrade-related issues (e.g., GraphStudio mappings disappearing after v2.4).
    
2. Performance surprises & timeouts. Naïve traversals/self-joins can be slower than expected; repeated executions sometimes hit the 60s query-timeout; cluster clock drift can also cause odd behavior. 

1. Version/Environment gotchas. Small version jumps can change behavior (e.g., code moved from 3.4→3.5 acting differently) and some GraphStudio actions run “interpreted mode,” which isn’t always supported.
    
2. Edge-case limitations in GSQL features. There are documented “known issues” around certain accumulator combinations (e.g., MapAccum with edge accumulators; ArrayAccum placement) and some features aren’t supported in distributed modes; exact float comparisons can also behave unexpectedly.