I have a single page JSF application using tabbing that has a large component tree (approx 8400 nodes). We are trying to get better performance out of it and are considering using iframes to break the component tree up into smaller trees (one for each tab) which I know has its own issues. Is there any other alternative to this to prune the tree to a reasonable size other than a complete re-architecture?
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I'm looking to visualize a large node network (well over a million nodes and edges) and am looking for the proper tool to do this. Cytoscape has many of the functionalities we're looking for, but it isn't clear if it will support this scale of network, especially for interactive use.
The most likely way for this to be feasible is to parallelize the visualization with multiple cpus or gpus. Can this be done in Cytoscape?
It's not clear if you have over a million nodes and their edges or a total count of over a million (nodes+edges). If the latter, as long as you have enough memory on your machine (and a little patience) Cytoscape can handle that fine. I've rendered graphs with well over that on my 32Gb Mac Laptop. The initial read and render of the graph might take a bit, but once it's rendered, the manipulation of the graph is quite fast. For really large graphs (>5,000,000 edges) I generally use a 64Gb desktop machine. Oh, and for layouts, I recommend the Prefuse force directed layout -- it scales the best...
-- scooter
Is the a way to physically separate between neo4j partitions?
Meaning the following query will go to node1:
Match (a:User:Facebook)
While this query will go to another node (maybe hosted on docker)
Match (b:User:Google)
this is the case:
i want to store data of several clients under neo4j, hopefully lots of them.
now, i'm not sure about whats is the best design for that but it has to fulfill few conditions:
no mixed data should be returned from a cypher query ( its really hard to make sure, that no developer will forget the ":Partition1" (for example) in a cypher query)
performance of 1 client shouldn't affect another client, for example, if 1 client has lots of data, and another client has small amount of data, or if a "heavy" query of 1 client is currently running, i dont want other "lite" queries of another client to suffer from slow slow performance
in other words, storing everything under 1 node, at some point in the future, i think, will have scalability problem, when i'll have more clients.
btw, is it common to have few clusters?
also whats the advantage of partitioning over creating different Label for each client? for example: Users_client_1 , Users_client_2 etc
Short answer: no, there isn't.
Neo4j has high availability (HA) clusters where you can make a copy of your entire graph on many machines, and then serve many requests against that copy quickly, but they don't partition a really huge graph so some of it is stored here, some other parts there, and then connected by one query mechanism.
More detailed answer: graph partitioning is a hard problem, subject to ongoing research. You can read more about it over at wikipedia, but the gist is that when you create partitions, you're splitting your graph up into multiple different locations, and then needing to deal with the complication of relationships that cross partitions. Crossing partitions is an expensive operation, so the real question when partitioning is, how do you partition such that the need to cross partitions in a query comes up as infrequently as possible?
That's a really hard question, since it depends not only on the data model but on the access patterns, which may change.
Here's how bad the situation is (quote stolen):
Typically, graph partition problems fall under the category of NP-hard
problems. Solutions to these problems are generally derived using
heuristics and approximation algorithms.[3] However, uniform graph
partitioning or a balanced graph partition problem can be shown to be
NP-complete to approximate within any finite factor.[1] Even for
special graph classes such as trees and grids, no reasonable
approximation algorithms exist,[4] unless P=NP. Grids are a
particularly interesting case since they model the graphs resulting
from Finite Element Model (FEM) simulations. When not only the number
of edges between the components is approximated, but also the sizes of
the components, it can be shown that no reasonable fully polynomial
algorithms exist for these graphs.
Not to leave you with too much doom and gloom, plenty of people have partitioned big graphs. Facebook and twitter do it every day, so you can read about FlockDB on the twitter side or avail yourself of relevant facebook research. But to summarize and cut to the chase, it depends on your data and most people who partition design a custom partitioning strategy, it's not something software does for them.
Finally, other architectures (such as Apache Giraph) can auto-partition in some senses; if you store a graph on top of hadoop, and hadoop already automagically scales across a cluster, then technically this is partitioning your graph for you, automagically. Cool, right? Well...cool until you realize that you still have to execute graph traversal operations all over the place, which may perform very poorly owing to the fact that all of those partitions have to be traversed, the performance situation you're usually trying to avoid by partitioning wisely in the first place.
Just started my excursion to graph processing methods and tools. What we basically do - count some standard metrics like pagerank, clustering coefficient, triangle count, diameter, connectivity etc. In the past was happy with Octave, but when we started to work with graphs having let's say 10^9 nodes/edges we stuck.
So the possible solutions can be distributed cloud made with Hadoop/Giraph, Spark/GraphX, Neo4j on top of them, etc.
But since I am a beginner, can someone advise what actually to choose? I did not get the difference when to use Spark/GraphX and when Neo4j? Right now I consider Spark/GraphX, since it have more Python alike syntax, while neo4j has the own Cypher. Visualization in neo4j is cool but not useful in such a large scale. I do not understand is there a reason to use additional level of software (neo4j) or just use Spark/GraphX? Since I understood neo4j will not save so much time like if we worked with pure hadoop vs Giraph or GraphX or Hive.
Thank you.
Neo4J: It is a graphical database which helps out identifying the relationships and entities data usually from the disk. It's popularity and choice is given in this link. But when it needs to process the very large data-sets and real time processing to produce the graphical results/representation it needs to scale horizontally. In this case combination of Neo4J with Apache Spark will give significant performance benefits in such a way Spark will serve as an external graph compute solution.
Mazerunner is a distributed graph processing platform which extends Neo4J. It uses message broker to process distribute graph processing jobs to Apache Spark GraphX module.
GraphX: GraphX is a new component in Spark for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD by introducing a new Graph abstraction: a directed multigraph with properties attached to each vertex and edge. It supports multiple Graph algorithms.
Conclusion:
It is always recommended to use the Hybrid combination of Neo4j with GraphX as they both easier to integrate.
For real time processing and processing large data-sets, use neo4j with GraphX.
For simple persistence and to show the entity relationship for a simple graphical display representation use standalone neo4j.
Neo4j: I have not used it, but I think it does all of a graph computation (like pagerank) on a single machine. Would that be able to handle your data set? It may depend on whether your entire graph fits into memory, and if not, how efficiently does it process data from disk. It may hit the same problems you encountered with Octave.
Spark GraphX: GraphX partitions graph data (vertices and edges) across a cluster of machines. This gives you horizontal scalability and parallelism in computation. Some things you may want to consider: it only has a Scala API right now (no Python yet). It does PageRank, triangle count, and connected components, but you may have to implement clustering coefficent and diameter yourself, using the provided graph API (pregel for example). The programming guide has a list of supported algorithms: https://spark.apache.org/docs/latest/graphx-programming-guide.html
GraphX is more of a realtime processing framework for the data that can be (and it's is better when) represented in a graph form. With GraphX you can use various algorithms that require large amounts of processing power (both RAM and CPU), and with neo4j you can (reliably) persist and update that data. This is what I'd suggest.
I know for sure that #kennybastani has done some pretty interesting advancements in that area, you can take a look at his mazerunner solution. It's also shipped as a docker image, so you can poke at it with a stick and find out for yourself whether you like it or not.
This image deploys a container with Apache Spark and uses GraphX to
perform ETL graph analysis on subgraphs exported from Neo4j. The
results of the analysis are applied back to the data in the Neo4j
database.
I am trying to implement a graph in dart.
I thought of creating the classes Node (vertex), Edge and Graph.
The main idea is that the Graph has a List of Nodes and a List of Edges.
Later I will implement some search algorithms on the graph.
I think of also adding a List of neighbours to each Node (List neighbours) so each Node knows its neighbours (successor nodes to be precise) . My thought here is that getting the successor nodes of one node is quicker when the node has this information than when the algorithm has to check the edge list each time. I know that changes (deletion of edges, nodes, adding new edges, node) would also cost more because I'd have to update them in two locations. But at the moment I don't plan to make the graph too dynamic after its creation.
Do you think this approach makes sense or might my way have some mayor flaw?
Even if you're not changing the graph once it's created, by denormalizing the graph you're creating Technical Debt, making it more complicated/difficult to work on. You could get some weird bugs that would be hard to track down. When you come back to this piece of code in a month or two it'll be a little more confusing since it's not fresh in your memory and it's not intuitive.
You would have to have an absurd amount of nodes to realize any performance gain and if you had an absurd amount of nodes you'd be doubling the amount of references to the edges, increasing the memory footprint. Also if you're compiling to JavaScript, be nice to the garbage collector by not having more references to an object than you need.
If you want to improve the performance of the graph I'd look into what I could run concurrently with isolates. Just keep in mind, graphs can get stupid complex so if you can keep anything simple, then keep it simple.
I have created a basic implementation of high level client over Neo4J (https://github.com/impetus-opensource/Kundera/tree/trunk/kundera-neo4j) and want to compare its performance with Native neo4j driver (and maybe SpringData too). This way I would be able to determine overhead my library is putting over native driver.
I plan to create an extension of YCSB for Neo4J.
My question is: what should be considered as a basic unit of object to be written into neo4j (should it be a single node or a couple of nodes joined by an edge).
What's current practice in Neo4J world. How people benchmarking neo4j performance are doing it.
There's already been some work for benchmarking Neo4J with Gatling: http://maxdemarzi.com/2013/02/14/neo4j-and-gatling-sitting-in-a-tree-performance-t-e-s-t-ing/
You could maybe adapt it.
See graphdb-benchmarks
The project graphdb-benchmarks is a benchmark between popular graph dataases. Currently the framework supports Titan, OrientDB, Neo4j and Sparksee. The purpose of this benchmark is to examine the performance of each graph database in terms of execution time. The benchmark is composed of four workloads, Clustering, Massive Insertion, Single Insertion and Query Workload. Every workload has been designed to simulate common operations in graph database systems.
Clustering Workload (CW): CW consists of a well-known community detection algorithm for modularity optimization, the Louvain Method. We adapt the algorithm on top of the benchmarked graph databases and employ cache techniques to take advantage of both graph database capabilities and in-memory execution speed. We measure the time the algorithm needs to converge.
Massive Insertion Workload (MIW): Create the graph database and configure it for massive loading, then we populate it with a particular dataset. We measure the time for the creation of the whole graph.
Single Insertion Workload (SIW): Create the graph database and load it with a particular dataset. Every object insertion (node or edge) is committed directly and the graph is constructed incrementally. We measure the insertion time per block, which consists of one thousand edges and the nodes that appear during the insertion of these edges.
Query Workload (QW): Execute three common queries:
FindNeighbours (FN): finds the neighbours of all nodes.
FindAdjacentNodes (FA): finds the adjacent nodes of all edges.
FindShortestPath (FS): finds the shortest path between the first node and 100 randomly picked nodes.
One way to performance-test is to use e.g. http://gatling-tool.org/. There is work underway to create benchmark frameworks at http://ldbc.eu . Otherwise, benchmarking is highly dependent on your domain dataset and the queries you are trying to do. Maybe you could start at https://github.com/neo4j/performance-benchmark and improve on it?