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Dgraph vs neo4j8/31/2023 ![]() I remember some example- Library- Books sorted and placed in Racks and each rack labeled like 'Graph Books, DB Books, OS, Server, Programming like. Neo4J stored data with its relations as we knew. Just one more point to share my learning since it wud help you other scenario. I was trying to get the actual Memory estimation technique link but missed to book mark.īut you pls check this below though it might be just 1% cause for the issue. Initial Heap_Size 8 GB and made it Heap_Max to 12 GB = 50% of server Memory Then we realized logic and importance of checking Heap Size + Page Cache+ OS memory (min 2-3 GB should be left for OS)Įxample like - Instance got 24 GB RAM, recommended configuration is like so even simple MATCH query took too much time. ![]() We made HeapSize and page Cache but sum of these 2 config value was equal to RAM of Server itself so OS have no space to allocate for its I/O and process. Memory = Heap Size + Page Cache + 2-3 GB for OS Please re-check whats your server RAM config. We faced similar issue (a blind mistake) since we ignored the memory config while setting up initial instance (AWScloud) so thought to confirm is that same on your side. ![]() So when you try COUNT(DISTINCT(node)) obvious it will load all ur nodes into memory and need more computing since you are using 'DISTINCT'. Aggregated operations always have some performance issue (Graph vs RDBMS ). Let me go through in detail and update but got the coreġ.Query Execution time is your concern and wants to ensure number of relationship count to super node may be the issue. Really i like your kind way to explain each and every point. Hope it may get you some thoughts to redefine the model. When we have dense node- connecting millions of relations.best approach wud be having relations by Month or category. Validate when we can turn property to Node or Node to Property- Based on whats your Business question This leads to me to think -unique ID for each person right? so better to have this as property in my the Node itself - instead of relations which actually reduces all these millions of paths What is the query you are trying to get from here - Any Data Model - Best way to design based on the Buesiness Question you are trying to doįrom your Business need, I understand you need to have unique relations for each node. ![]() Hi, though i may yet get complete understanding on this domain/context but few questions here pls Our business domain requires that each of the unique person nodes must have one relationship to each of the unique A_Law nodes I also read and watched the posts and videos listed below multiple times, but I still don't know how else to improve on the data model: Neo4j Graph Data Platform I've tried profiling and explaining our queries and I see that there are billions of rows being accessed. Here's a warning I read about adding too many indices: Welcome to the Dark Side: Neo4j Worst Practices (& How to Avoid Them) This results in 500,000,000 relationships and this is causing our cypher queries to take more than an hour to finish.Īnother approach that I'll implement here is to add more indices to the properties of the nodes or relationships, but I would like to be careful when adding various indices. Our business domain requires that each of the unique person nodes must have one relationship to each of the unique A_Law nodes. Therefore, we have 100,000 times 5,000 is 500,000,000) with 0 propertiesī_Access: 2,000 relationships with 0 propertiesĬ_Access: 3,000 relationships with 0 propertiesĪ_Rel: 5,000 relationships with 2 propertiesī_Rel: 3,000 relationships with 2 propertiesĬ_Re : 2,000 relationships with 2 propertiesĪs you can see above, the problem here is with the number of the A_Access relationship that has exploded as a result of the product between all of the person nodes to all of the A_Law nodes. In this example, here are the counts for each of the nodes and relationships:Ī_Access: 500,000,000 relationships (every person node is connected to every single unique A_Law node.
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