Unhook the small drain lines that connect couplers together and drain down into oil fill neck. Unhook any implement hoses, start tractor, have someone move the hydraulic levers back and forth one at a time. He said he had to work with it for quite a while.Posted 8/28/2012 08:16 (#2562645 - in reply to #2562525) Subject: Re: John Deere 6420 hydraulic issuesYou need to replace the coupler assemblies that are threaded into rear valves. I tried in vain to put these in myself and finally had to get someone with experience to put them in. John deere remote hydraulic leak.

1. Cassandra Vs Mysql Performance
2. Cassandra Read Vs Write Performance Free
3. Cassandra Read Vs Write Performance Test

Peter Schuller Since garbage collection is logged (if you're running with default settings etc), any multi-second GC:s should be discoverable in said log. So for testing that hypothesis i'd check there first. Cassandra itself logs GC:s, but you can also turn of the JVM:s GC logging by e.g. '-XX:+PrintGC -XX:+PrintGCDetails -XX:+PrintGCTimestamps'. I did some batch write testing to see how it scaled up to about 200 million rows and 200 gb; I had ocational spikes in latency that were due to disk writes being. I'm also interested in comparing notes with anyone else that has been doingread/write throughput benchmarks with Cassandara.I did some batch write testing to see how it scaled up to about 200million rows and 200 gb; I had ocational spikes in latency that weredue to disk writes being flushed by the OS.

Cassandra Vs Mysql Performance

The third diagram for Workload F shows the results of read-modify-write operations. Cassandra and HBase have similar performance. MongoDB’s latency increases together with the workload, up to the maximum throughput of roughly 1,000 operations per second. We can conclude that Cassandra and Hbase are pretty good at dealing with mixed workloads.

Cassandra Read Vs Write Performance Free

However it was probablyexacerbated in this case by the fact that this was ZFS/FreeBSD and ZFSis always (in my humble of opinion, and at least on FreeBSD)exhibiting the behavior for me that it flushes writes too late and endup blocking applications even if you have left-over bandwidth.In my case I 'eliminated' the issue for the purpose of my test byhaving a stupid while loop simply doing 'sync' every handful ofseconds, to avoid accumulating too much data in the cache.While I expect this to be less of a problem for other setups, it'spossible this is what you're seeing. If the operating system isblocking writes to the commit log for example (are you running withperiodic fsync or batch wise fsync?).

This is how we got a big win by switching from MongoDB to Cassandra formanaging our time series data.Backgroundhas an AWS cloud-hosted data processingplatform powered in part by a chain of Resque workers.Being a telematics company, we process a lot of time series data, and theinitial pipeline that was built did a lot of work with MongoDB. This choice wasmade early on and it was supposed to be a temporary one.MongoDB behaved reasonably well, but the unpredictability of the load times fordifferent sizes of work was troubling and made pipeline tuning difficult. Somequeries would return 30 documents and others 300. With the way Mongo isdesigned (and most relational datastores also), this resulted in a varying IOload. The query for 30 documents would return much faster than the query for. Neither was particularly quick, either, even with good indexing andrelatively decent instances.Futhermore, we were having to scale our Mongo instances vertically to at leastan extent, and we weren’t excited by the long term economy of doing that. Inorder to get decent performance we were already running multiple extra largeinstances.

Cassandra Read Vs Write Performance Test

SolutionFrom the beginning we wanted to use Cassandra, which I had previously run atstarting from version 0.5. It has always provenitself to be intensely reliable, predictable, and resilient. Those features,combined with the horizontal scalability make it a pretty killer data store.The normal data flow is that we write some data, read it back, modify it, writeit, and read it back one more time. These actions happen in different workers.This isn’t the ideal workload for Cassandra, but it’s a reasonably good fitbecause of how we query it.Cassandra is really good for time-series data because you can write one columnfor each period in your series and then query across a range of time usingsub-string matching.

This is best done using columns for each period ratherthan rows, as you get huge IO efficiency wins from loading only a single rowper query. Cassandra then has to at worst do one seek and then read for all theremaining data as it’s written in sequence to disk.We designed our schema to use IDs that begin with timestamps so that they canbe range queried over arbitrary periods like this, with each row representingone record and each column representing one period in the series.

1. Ways to improve Cassandra read performance in my scenario. I am not sure what other things I should try it out with Cassandra to get much better read performance. I am assuming it is hitting the disk in my case. Should I try increasing the Replication Factor to some higher number? Tuning write performance in cassandra.
2. Moreover, the actual measurements of Cassandra’s write performance (in a 32-node cluster, almost 326,500 operations per second versus HBase’s 297,000) also prove that Cassandra is better at writes than HBase. Read; If you need lots of fast and consistent reads (random access to data and scans), then you can opt for HBase.

All data isthen available to be queried on a row key and start and end times.With the way our workload behaves, it seems that with MongoDB the number ofwrites was noticeably impacting read performance. With Cassandra, this does notseem to be the case for our scenario. ComparisonThe graph below shows the better part of a year of performance, and the blueline is the one most affected by the performance of the data store.

It shouldbe obvious from the number of drops in the times that we were both tuningMongoDB and scaling the hardware to keep performance reasonable. But none ofthese changes were as effective as the move to Cassandra.We phased MongoDB out by first turning off writes to it, then eventuallyturning off reads from it as well. These are clearly marked on the graph.Worth noting is that not only did the performance massively improve (for aroundthe same AWS spend, it should be said), but the predictability is hugely betteras well. Notice how much flatter that blue line is on the right end of thegraph compared to when Mongo was in heavy use.One thing that is not at all obvious from the graph is that the system was alsounder massively heavier strain after the switch to Cassandra because of additionalbulk processing going on in the background.For us this has been an unequivocally big win and I thought it was worthsharing the results.