neo4j high cpu and open transactions - neo4j

is there a way to check why the server (neo4j dedicated) has high cpu after a while of running queries?
also is the attached monitor screen ok? lots of open transactions there, which only increase

Opened should continue to increase. That is not how many are currently opened but rather just a total including transactions that were opened and are now finished and not running.
However, "current" shows 7 which means you still have 7 transactions running which probably explains the high CPU usage, depending on what those transactions are doing. Is it expected that you would have 7 transactions running? If so then there's probably nothing to worry about. If not, then you might want to look in to why those transactions didn't finish when you expected them to and you can also configure the execution card to limit the time each query can run for before being killed.

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Reliability with Neo4J- Is there a way to configure neo4j to not crash?

I am evaluating neo4j for production usage. During my playing around with neo4j it crashed (OutOfMemory exceptions) many times due to non-optimized queries.
I understand that perhaps there's not enough memory. In other databases I've used the server doesn't just crash, but instead slows down or terminates a specific query/transaction. Neo4j on the other hand simply shuts down.
Is there a way to configure neo4j to not crash?
Here's a response I received from the neo4j team:
you can configure several options, that we e.g. have also enabled in
sandbox:
transaction and global memory limits, the global memory limit should be around 70% of the configured heap, and if you know what the
concurrency of your statements is you can also configure the
per-statement memory limit if you don't have outlier queries
transaction timeouts, limit queries to at most X seconds runtime and abort them after
Those settings should be default for new installations but
unfortunately aren't yet.
Source
You will want to control and limit transaction memory usage, see : https://neo4j.com/docs/operations-manual/current/performance/memory-configuration/#memory-configuration-limit-transaction-memory

Neo4J taking out long-lived locks in non-query transaction

In our application we occasionally add around 10,000 nodes and 100,000 relationships to a Neo4J graph over the course of a few minutes, and then DETACH DELETE many of them a few minutes later. Previously the delete query was very quick (<100ms), but after a small change to our data model and some of our other queries (which are not running at the time), it now often blocks for minutes before completing.
While this blocking is happening there are no other queries running, and I have an export from Halin showing all the transactions that are happening at the time. It's difficult to reproduce here, but in summary there are exactly two transactions going on, one of which is my delete query. The delete query is stated to be blocked by the other one, which has 7 locks out, is in the Running state, and has no attached query or client at all. I imagine this means that it's an internal Neo4J process. It has 0 cpu time, and its entire 180s runtime is accounted for by idle time. There's no other information given.
What could be causing this transaction to lock the nodes that I want to delete for such a long time with no queries running?
What I've tried:
Using apoc.periodic.iterate and apoc.periodic.commit to split the query into smaller chunks - the inner queries end up locked
Looking in the query logs - difficult to be sure but I can't see any evidence of the internal transaction
Looking in the debug logs - records of garbage collections (always around 300ms) and some graph algorithms running, but never while this query is blocked, and nothing else relevant
Other info:
Neo4J version: 3.5.18-enterprise (docker)
Cluster mode: HA cluster with 2 nodes (also reproduced with only 1 node)
It turned out that there was a query a few minutes before that had been set going and then the client disconnected (missing await in C#). I still don't quite understand why this caused the observations, but my guess is that Neo4j put the query into a weird state after the client disconnected, and then some part of it ended up waiting for the transaction timeout before releasing its locks.

ruby requests more memory when there are plenty free heap slots

We have a server running
Sidekiq 4.2.9
rails 4.2.8
MRI 2.1.9
This server periodically produce some amount of importing from external API's, perform some calculations on them and save these values to the database.
About 3 weeks ago server started hanging, as I see from NewRelic (and when ssh'ed to it) - it consumes more and more memory over time, eventually occupying all available RAM, then server hangs.
I've read some articles about how ruby GC works, but still can't understand, why at ~5:30 AM heap size jumps from ~2.3M to 3M , when there's still 1M free heap slots available(GC settings are default)
similar behavior, 3:35PM:
So, the questions are:
how to make Ruby fill free heap slots instead of requesting new slots from OS ?
how to make it release free heap slots to the system ?
how to make Ruby fill free heap slots instead of requesting new slots from OS ?
Your graph does not have "full" fidelity. It is a lot to assume that GC.stat was called by Newrelic or whatnot just at the exact right time.
It is incredibly likely that you ran out of slots, heap grew and since heaps don't shrink in Ruby you are stuck with a somewhat bloated heap.
To alleviate some of the pain you can limit RUBY_GC_HEAP_GROWTH_MAX_SLOTS to a sane number, something like 100,000 will do, I am trying to lobby setting a default here in core.
Also
Create a persistent log of jobs that run and time they ran (duration and so on), gather GC.stat before and after job runs
Split up your jobs by queue, run 1 queue on one server and other queue on another one, see which queue and which job is responsible for the problem
Profile various jobs you have using flamegraph or other profiling tools
Reduce the amount of concurrent jobs you run as an experiment, or place a mutex between certain job types. It is possible that 1 "job a" at a time is OKish, and 20 concurrent "job a"s at a time will bloat memory.

How to debug Neo4J stalls?

I have a Neo4J server running that periodically stalls out for 10s of seconds. The web frontend will say it's "disconnected" with the red warning bar at the top, and a normally instant REST query in my application will apparently hang, until the stall ends and then everything returns to normal. The web frontend becomes usable and my REST query completes fine.
Is there any way to debug what is happening during one of these stall periods? Can you get a list of currently running queries? Or a list of what hosts are connected to the server? Or any kind of indication of server load?
Most likely JVM garbage collection kicking in because you haven't allocated enough heap space.
There's a number of ways to debug this. You can for example enable GC logging (uncomment appropriate lines in neo4j-wrapper.conf), or use a profiler (e.g. YourKit) to see what's going on and why the pauses.

My server gets overloaded even though I keep a limit on the requests I send it

I have a server on Heroku - 3 dynos, 2 processes each.
The server does 2 things:
It responds to requests from the browser (AJAX and some web pages), based on data stored in a postgresql database
It exposes a REST API to update the data in the database. This API is called by another server. The rate of calls is limited: The other server only calls my server through a queue with a single worker, which makes sure the other server doesn't issue more than one request in parallel to my server (I verified that indeed it doesn't).
When I look at new relic, I see the following graph, which suggests that even though I keep the other server at one parallel request at most, it still loads my server which creates peaks.
I'd expect that since the rate of calls from the other server is limited, my server will not get overloaded, since a request will only start when the previous request ended (I'm guessing that maybe the database gets overloaded if it gets an update request and returns but continue processing after that).
What can explain this behaviour?
Where else can I look at in order to understand what's going on?
Is there a way to avoid this behaviour?
There are whole lot of directions this investigation could go, but from your screenshot and some inferences, I have two guesses.
A long query—You'd see this graph if your other server or a browser occasionally hits a slow query. If it's just a long read query and your DB isn't hitting its limits, it should only affect the process running the query, but if the query is taking an exclusive lock, all dynos will have to wait on it. Since the spikes are so regular, first think of anything you have running on a schedule - if the cadence matches, you probably have your culprit. The next simple thing to do is run heroku pg:long-running-queries and heroku pg:seq-scans. The former shows queries that might need optimization, and the latter shows full table scans you can probably fix with a different query or a better index. You can find similar information in NewRelic's Database tab, which has time and throughput graphs you can try to match agains your queueing spikes. Finally, look at NewRelic's Transactions tab.
There are various ways to sort - slowest average response time is probably going to help, but check out all the options and see if any transactions stand out.
Click on a suspicious transaction and look at the graph on the right. If you see spikes matching your queueing buildups, that could be it, but since it looks to be affecting your whole site, watch out for several transactions seeing correlated slowdowns.
Check out the transaction traces at the bottom. Something in there taking a long time to run is as close to a smoking gun as you'll get. This should correlate with pg:long-running-queries.
Look at the breakdown table between the graph and the transaction traces. Check for things that are taking a long time (eg. a 2 second external request) or happening often (eg, a partial that gets rendered 2500 times per request). Those are places for caching or optimization.
Garbage collection—This is less likely because Ruby GCs all the time and there's no reason it would show spikes on that regular cadence, but if there's a regular request that allocates a ton of objects, both building the objects and cleaning them up will take time. It would only affect one dyno at once, and it would be correlated with a long or highly repetitive query in your NewRelic investigation. You can see some stats about this in NewRelic's Ruby VM tab.
Take a look at your dyno and DB memory usage too. Both are printed to the Heroku logs, and if you add Librato, they'll build some automatic graphs that are quite helpful. If your dyno is swapping, performance will suffer and you should either upgrade to a bigger dyno or run fewer processes per dyno. Processes will typically accumulate memory as they run and never quite release as much as you'd like, so tune it so that right before a restart, your dyno is just under its available RAM. Similarly for the DB, if you're hitting swap there, query performance will suffer and you should upgrade.
Other things it could be, but probably isn't in this case:
Sleeping dynos—Heroku puts a dyno to sleep if it hasn't served a request in a while, but only if you have just 1 dyno running. You have 3, so this isn't it.
Web Server Concurrency—If at any given moment, there are more requests than available processes, requests will be queued. The obvious fix is to increase the available dynos/processes, which will put more load on your DB and potentially move the issue there. Since some regular request is visible every time, I'm guessing request volume is low and this also isn't your problem.
Heroku Instability—Sometimes, for no obvious reason, Heroku starts queueing requests more than it should and doesn't report any issues at status.heroku.com. Restarting the dynos typically fixes that temporarily while Heroku gets their head back on straight.

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