Our own multi-model database – part 2 _ max de marzi

So before going much further I decided to benchmark our new database and found that our addNode speed is phenomenal, but it was taking forever to create relationships. Data recovery raid 5 See some JMH benchmarks below:

Each time I was creating 1000 users, so this test shows us we can create over a million empty nodes in one second. Database architect Yeah ChronicleMap is damn fast. Data recovery options But then when I tried to create 100 relationships for each user (100,000 total) it was taking forever (about 6 seconds).

Database jobs So I opened up YourKit and you won’t believe what I found out next (come on that’s some good clickbait).

The cost of updating any value is the same as deserializating the whole object and reserializing it again. H2 database file The larger the value the more expensive it is. R studio data recovery serial key It sounds like what you really need is a multi-map where you can have multiple values for the same key. Database query languages — Peter Lawrey

But of course. P d database Every time I added a relationship to the Set value in each ChronicleMap key it has to read it, open it up, change it, and save it back down again, and I was doing this twice one for the outgoing and one for the incoming relationship.

Ok, so Peter suggests a Multi-Map… but what I really need is a Multi-Map that can be reversed… so as any developer would do I turn to StackOverflow.

Yes, that sounds good and Slavik gives us an implementation that’s almost what I needed. Database 101 The only thing missing was not just finding a key by value, but storing the reverse of the relationship in a second MultiMap. M power database So I tweaked it to do just that and called it a ReversibleMultiMap.

I decided I wanted to leave the train wreck of the implementation I wrote yesterday alone and started a new repository. Data recovery from external hard drive The ArrayListMultimap above comes from the Google Guava Library which you’ve seen me use before on this blog.

So somehow mixing Guava and Chronicle I ended up with GuancicleDB, but Google told me what I really wanted was GuancialeDB which is some kind of cured meat, so that’s our new name.

Look at that amazing logo. Database join types You can cut it up in cubes, you can slice it, you can just eat it whole like a lion. Section 8 database It exemplifies the multi-model nature of our database. Icd 9 database Ok, listen. Database xampp All the good names are taken. Database administrator jobs Just ask the CockRoachDB guys. Data recovery joondalup Besides, we can change the name later, it’s not like we added a stupid “4j” to the thing.

In case you haven’t figured it out yet, the reason for storing the relationships both ways is that I’m kinda trying to replicate what we do at Neo4j. Database of genomic variants I hope you have seen this internals blog post before, but if you haven’t go ahead and take a look. Database viewer The important piece is the last image shown below:

Neo4j uses fixed sized records, so we know node 12 is at some offset location plus 12 * NODE_SIZE (which I think is around 16 or 17 bytes) and relationship 99 is at some offset location plus 99 * RELATIONSHIP_SIZE (which I think is around 33 or 34 bytes). H data recovery registration code free download This makes it easy to get from one spot to the next in O(1) without an index. Database hardware We aren’t using an index which is O(log n), we’re using a hash, so it’s somewhere between O(1) and O(log n) in the worst case.

This dual relationship storage costs us in terms of space and complicates methods like removeNode, but this ReversibleMultiMap makes things a bit easier. Database roles Compare this method with the previous one:

Here, we first remove the node from our “nodes” ChronicleMap. B tree database management system Then for each relationship type we get its outgoing relationships and grab their target nodes. Database file Now we can delete any relationships that had properties from the “relationships” ChronicleMap and then we do the reverse relationships properties and finally remove the relationships themselves with removeAll. Data recovery near me Ok, so what about performance. Database job description Well our create node speed is about the same since that part hasn’t changed, but oh my take a look at the relationships.

We are now doing about 48 operations per second where before it took us 6 seconds to do 1 operation (remember we are creating 100k relationships in each operation). Data recovery 94fbr That’s like almost 300 times faster write speed. Database foreign key How well does it perform reading? Well how about an actual traversal, a recommendation. Database as a service We are going to start with the items I like, then find other people who liked those items, and then we’re going to find things those people liked that I haven’t liked yet, count them up and return the top 10.

We will vary the number of items, the number of likes, and the people doing the liking for our tests. Iphone 6 data recovery I’ve modified the likesCount column to show likes per person and total likes. Database google drive Take a look at the results below:

The sparser our graph is the faster this query returns. Data recovery geek squad You can see this comparing our fastest two results which both have 1000 people, and 10k relationships but vary in 200/2000 items. Database recovery pending You can see this again in our worst two results where the 2000 item graph is sparser and faster.

I think this is workable for now. Data recovery prices Sorry for the first false start, but I learn more from my screw-ups than when I get it right the first time, and I hope you do too. Database sharding So stay tuned for next time, when we will put a web server in front of this thing and add an API.