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- 1. Copyright © ArangoDB GmbH, 2016 Handling Billions Of Edges in a Graph Database Michael Hackstein @mchacki
- 2. Copyright © ArangoDB GmbH, 2016 ‣ Schema-free Objects (Vertices) ‣ Relations between them (Edges) ‣ Edges have a direction { name: "alice", age: 32 } { name: "dancing" } { name: "bob", age: 35, size: 1,73m } { name: "reading" } { name: "fishing" } hobby hobby hobby hobby
- 3. Copyright © ArangoDB GmbH, 2016 ‣ Schema-free Objects (Vertices) ‣ Relations between them (Edges) ‣ Edges have a direction ‣ Edges can be queried in both directions ‣ Easily query a range of edges (2 to 5) ‣ Undeﬁned number of edges (1 to *) ‣ Shortest Path between two vertices { name: "alice", age: 32 } { name: "dancing" } { name: "bob", age: 35, size: 1,73m } { name: "reading" } { name: "fishing" } hobby hobby hobby hobby
- 4. Copyright © ArangoDB GmbH, 2016 ‣ Give me all friends of Alice Bob Charly DaveAlice Eve Frank
- 5. Copyright © ArangoDB GmbH, 2016 ‣ Give me all friends of Alice Bob Charly DaveCharly Bob DaveAlice Eve Frank
- 6. Copyright © ArangoDB GmbH, 2016 ‣ Give me all friends-of-friends of Alice Alice Bob Charly Eve Dave Frank
- 7. Copyright © ArangoDB GmbH, 2016 ‣ Give me all friends-of-friends of Alice Alice Bob Charly Eve Dave FrankEve Frank
- 8. Copyright © ArangoDB GmbH, 2016 ‣ What is the linking path between Alice and Eve Alice Bob Charly Eve Dave Frank
- 9. Copyright © ArangoDB GmbH, 2016 ‣ What is the linking path between Alice and Eve Alice Bob Charly Eve Dave FrankBobEve Alice
- 10. Copyright © ArangoDB GmbH, 2016 ‣ Which Trainstations can I reach if I am allowed to drive a distance of at most 6 stations on my ticket You are here
- 11. Copyright © ArangoDB GmbH, 2016 ‣ Which Trainstations can I reach if I am allowed to drive a distance of at most 6 stations on my ticket You are here
- 12. Copyright © ArangoDB GmbH, 2016 Friend ‣ Give me all users that share two hobbies with Alice Alice Hobby1 Hobby2
- 13. Copyright © ArangoDB GmbH, 2016 FriendFriend ‣ Give me all users that share two hobbies with Alice Alice Hobby1 Hobby2
- 14. Copyright © ArangoDB GmbH, 2016 Alice Product ProductFriend ‣ Give me all products that at least one of my friends has bought together with the products I already own, ordered by how many friends have bought it and the products rating, but only 20 of them. has_bought has_bought has_boughtis_friend
- 15. Copyright © ArangoDB GmbH, 2016 Alice Product ProductFriend Product ‣ Give me all products that at least one of my friends has bought together with the products I already own, ordered by how many friends have bought it and the products rating, but only 20 of them. has_bought has_bought has_boughtis_friend
- 16. Copyright © ArangoDB GmbH, 2016
- 17. Copyright © ArangoDB GmbH, 2016 ‣ Give me all users which have an age attribute between 21 and 35.
- 18. Copyright © ArangoDB GmbH, 2016 ‣ Give me all users which have an age attribute between 21 and 35. ‣ Give me the age distribution of all users
- 19. Copyright © ArangoDB GmbH, 2016 ‣ Give me all users which have an age attribute between 21 and 35. ‣ Give me the age distribution of all users ‣ Group all users by their name
- 20. Copyright © ArangoDB GmbH, 2016
- 21. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) S
- 22. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S B C A S
- 23. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S ‣ We apply ﬁlters on edges B C A SS A B
- 24. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S ‣ We apply ﬁlters on edges ‣ We iterate down one of the new vertices (A) D E B C A SS A B
- 25. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S ‣ We apply ﬁlters on edges ‣ We iterate down one of the new vertices (A) ‣ We apply ﬁlters on edges D E B C A SS E A B
- 26. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S ‣ We apply ﬁlters on edges ‣ We iterate down one of the new vertices (A) ‣ We apply ﬁlters on edges ‣ The next vertex (E) is in desired depth. Return the path S -> A -> E D E B C A SS E A B
- 27. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S ‣ We apply ﬁlters on edges ‣ We iterate down one of the new vertices (A) ‣ We apply ﬁlters on edges ‣ The next vertex (E) is in desired depth. Return the path S -> A -> E ‣ Go back to the next unﬁnished vertex (B) D E B C A SS E A B
- 28. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S ‣ We apply ﬁlters on edges ‣ We iterate down one of the new vertices (A) ‣ We apply ﬁlters on edges ‣ The next vertex (E) is in desired depth. Return the path S -> A -> E ‣ Go back to the next unﬁnished vertex (B) ‣ We iterate down on (B) D E F B C A SS E A B
- 29. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S ‣ We apply ﬁlters on edges ‣ We iterate down one of the new vertices (A) ‣ We apply ﬁlters on edges ‣ The next vertex (E) is in desired depth. Return the path S -> A -> E ‣ Go back to the next unﬁnished vertex (B) ‣ We iterate down on (B) ‣ We apply ﬁlters on edges D E F B C A SS E A F B
- 30. Copyright © ArangoDB GmbH, 2016 ‣ We ﬁrst pick a start vertex (S) ‣ We collect all edges on S ‣ We apply ﬁlters on edges ‣ We iterate down one of the new vertices (A) ‣ We apply ﬁlters on edges ‣ The next vertex (E) is in desired depth. Return the path S -> A -> E ‣ Go back to the next unﬁnished vertex (B) ‣ We iterate down on (B) ‣ We apply ﬁlters on edges ‣ The next vertex (F) is in desired depth. Return the path S -> B -> F D E F B C A SS E A F B
- 31. Copyright © ArangoDB GmbH, 2016 ‣ Once: ‣ Find the start vertex ‣ For every depth: ‣ Find all connected edges ‣ Filter non-matching edges ‣ Find connected vertices ‣ Filter non-matching vertices Depends on indexes: Hash: Edge-Index or Index-Free: Linear in edges: Depends on indexes: Hash: Linear in vertices: Only one pass: O 1 1 n n * 1 n 3n
- 32. Copyright © ArangoDB GmbH, 2016 ‣ Linear sounds evil? ‣ NOT linear in All Edges O(E) ‣ Only Linear in relevant Edges n < E ‣ Traversals solely scale with their result size ‣ They are not eﬀected at all by total amount of data ‣ BUT: Every depth increases the exponent: O((3n)d ) ‣ "7 degrees of separation": (3n)6 < E < (3n)7
- 33. Copyright © ArangoDB GmbH, 2016 ‣ MULTI-MODEL database ‣ Stores Key Value, Documents, and Graphs ‣ All in one core ‣ Query language AQL ‣ Document Queries ‣ Graph Queries ‣ Joins ‣ All can be combined in the same statement ‣ ACID support including Multi Collection Transactions + +
- 34. Copyright © ArangoDB GmbH, 2016 FOR user IN users RETURN user
- 35. Copyright © ArangoDB GmbH, 2016 FOR user IN users FILTER user.name == "alice" RETURN user Alice
- 36. Copyright © ArangoDB GmbH, 2016 FOR user IN users FILTER user.name == "alice" FOR product IN OUTBOUND user has_bought RETURN product Alice
- 37. Copyright © ArangoDB GmbH, 2016 FOR user IN users FILTER user.name == "alice" FOR product IN OUTBOUND user has_bought RETURN product Alice has_bought TV
- 38. Copyright © ArangoDB GmbH, 2016 FOR user IN users FILTER user.name == "alice" FOR recommendation, action, path IN 3 ANY user has_bought FILTER path.vertices[2].age <= user.age + 5 AND path.vertices[2].age >= user.age - 5 FILTER recommendation.price < 25 LIMIT 10 RETURN recommendation Alice has_bought TV
- 39. Copyright © ArangoDB GmbH, 2016 FOR user IN users FILTER user.name == "alice" FOR recommendation, action, path IN 3 ANY user has_bought FILTER path.vertices[2].age <= user.age + 5 AND path.vertices[2].age >= user.age - 5 FILTER recommendation.price < 25 LIMIT 10 RETURN recommendation Alice has_bought TV has_bought playstation.price < 25 PlaystationBob alice.age - 5 <= bob.age && bob.age <= alice.age + 5 has_bought
- 40. Copyright © ArangoDB GmbH, 2016 ‣ Many graphs have "Supernodes" ‣ Vertices with many inbound and/or outbound edges ‣ Traversing over them is expensive ‣ Often you only need a subset of edges Bob Alice
- 41. Copyright © ArangoDB GmbH, 2016 ‣ Remember Complexity? O((3n)d ) ‣ Filtering of non-matching edges is linear for every depth ‣ Index all edges based on their vertices and arbitrary other attributes ‣ Find initial set of edges in identical time ‣ Less / No post-ﬁltering required ‣ This decreases the n signiﬁcantly Alice
- 42. Copyright © ArangoDB GmbH, 2016 ‣ We have the rise of big data ‣ Store everything you can ‣ Dataset easily grows beyond one machine ‣ This includes graph data!
- 43. Copyright © ArangoDB GmbH, 2016 ‣ Distribute graph on several machines (sharding) ‣ How to query it now? ‣ No global view of the graph possible any more ‣ What about edges between servers? ‣ In a shared environment network most of the time is the bottleneck ‣ Reduce network hops ‣ Vertex-Centric Indexes again help with super-nodes ‣ But: Only on a local machine
- 44. First let's do the cluster thingy
- 45. Copyright © ArangoDB GmbH, 2016 ‣ ArangoDB is the ﬁrst fully certiﬁed database including the persistence primitives for DC/OS ‣ ArangoDB's cluster resource management is based on Apache Mesos ‣ Later this year we will also support Kubernetes
- 46. Copyright © ArangoDB GmbH, 2016 ‣ ArangoDB can run clusters without it ‣ Setup Requires manual eﬀort (can be scripted) ‣ This works: ‣ Automatic Failover (Follower takes over if leader dies) ‣ Rebalancing of shards ‣ Everything inside of ArangoDB ‣ This is based on Mesos: ‣ Complete self healing ‣ Automatic restart of ArangoDBs (on new machines) ➡ We suggest you have someone on call
- 47. Demo Time DC/OS
- 48. Now distribute the graph
- 49. Copyright © ArangoDB GmbH, 2016 ‣ Only parts of the graph on every machine ‣ Neighboring vertices may be on diﬀerent machines ‣ Even edges could be on other machines than their vertices ‣ Queries need to be executed in a distributed way ‣ Result needs to be merged locally
- 50. Copyright © ArangoDB GmbH, 2016 Random Distribution ‣ Advantages: ‣ every server takes an equal portion of data ‣ easy to realize ‣ no knowledge about data required ‣ always works ‣ Disadvantages: ‣ Neighbors on diﬀerent machines ‣ Probably edges on other machines than their vertices ‣ A lot of network overhead is required for querying
- 51. Copyright © ArangoDB GmbH, 2016 Random Distribution ‣ Advantages: ‣ every server takes an equal portion of data ‣ easy to realize ‣ no knowledge about data required ‣ always works ‣ Disadvantages: ‣ Neighbors on diﬀerent machines ‣ Probably edges on other machines than their vertices ‣ A lot of network overhead is required for querying
- 52. Copyright © ArangoDB GmbH, 2016 Index-Free Adjacency ‣ Used by most other graph databases ‣ Every vertex maintains two lists of it's edges (IN and OUT) ‣ Do not use an index to ﬁnd edges ‣ How to shard this?
- 53. Copyright © ArangoDB GmbH, 2016 Index-Free Adjacency ‣ Used by most other graph databases ‣ Every vertex maintains two lists of it's edges (IN and OUT) ‣ Do not use an index to ﬁnd edges ‣ How to shard this?
- 54. Copyright © ArangoDB GmbH, 2016 Index-Free Adjacency ‣ Used by most other graph databases ‣ Every vertex maintains two lists of it's edges (IN and OUT) ‣ Do not use an index to ﬁnd edges ‣ How to shard this?
- 55. Copyright © ArangoDB GmbH, 2016 Index-Free Adjacency ‣ Used by most other graph databases ‣ Every vertex maintains two lists of it's edges (IN and OUT) ‣ Do not use an index to ﬁnd edges ‣ How to shard this? ????
- 56. Copyright © ArangoDB GmbH, 2016 Index-Free Adjacency ‣ ArangoDB uses an hash-based EdgeIndex (O(1) - lookup) ‣ The vertex is independent of it's edges ‣ It can be stored on a diﬀerent machine ‣ Used by most other graph databases ‣ Every vertex maintains two lists of it's edges (IN and OUT) ‣ Do not use an index to ﬁnd edges ‣ How to shard this? ????
- 57. Copyright © ArangoDB GmbH, 2016 Domain Based Distribution ‣ Many Graphs have a natural distribution ‣ By country/region for People ‣ By tags for Blogs ‣ By category for Products ‣ Most edges in same group ‣ Rare edges between groups
- 58. Copyright © ArangoDB GmbH, 2016 Domain Based Distribution ‣ Many Graphs have a natural distribution ‣ By country/region for People ‣ By tags for Blogs ‣ By category for Products ‣ Most edges in same group ‣ Rare edges between groups
- 59. Copyright © ArangoDB GmbH, 2016 Domain Based Distribution ‣ Many Graphs have a natural distribution ‣ By country/region for People ‣ By tags for Blogs ‣ By category for Products ‣ Most edges in same group ‣ Rare edges between groups ArangoDB Enterprise Edition uses Domain Knowledge for short-cuts
- 60. Copyright © ArangoDB GmbH, 2016 SmartGraphs - How it works DB Server 1 DB Server 2 DB Server n Coordinator Foxx Coordinator Foxx
- 61. Copyright © ArangoDB GmbH, 2016 SmartGraphs - How it works DB Server 1 DB Server 2 DB Server n Coordinator Foxx Coordinator Foxx
- 62. Copyright © ArangoDB GmbH, 2016 SmartGraphs - How it works DB Server 1 DB Server 2 DB Server n Coordinator Foxx Coordinator Foxx
- 63. Sneak Preview SmartGraphs
- 64. Copyright © ArangoDB GmbH, 2016
- 65. Copyright © ArangoDB GmbH, 2016 Database document write transactions per virtual CPU/second ArangoDB 1,730 Aerospike 2,500 Cassandra 965 Couchbase 1,375 FoundationDB 750 *) Source: https://mesosphere.com/blog/2015/11/30/arangodb-benchmark-dcos/
- 66. Copyright © ArangoDB GmbH, 2016 ‣ Further questions? ‣ Follow us on twitter: @arangodb ‣ Join our slack: slack.arangodb.com ‣ Follow me on twitter/github: @mchacki ‣ Write me a mail: michael@arangodb.com Thank You

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