How much data can the free version of Datomic handle in terms of storage and throughput? As far as I know, the free transactor uses H2 to store a local embedded database.
What stops me from using this in production, ignoring the obvious lack of storage redundancy and limited number of peers (1)?
Datomic Pro starter edition is probably more of a go-to than the free edition since it supports all of the storage services as the licensed pro version. In terms of local storage and throughput, I'd say it can store up to the amount of space is available on the disk. The transactor handles all writes, so I'd imagine your only potential bottlenecks for throughput would be the hard disk or, if you're throwing tons of transactions at it, it would be waiting for the indexing jobs to complete as your data grows. Indexing can get computationally expensive over time and it's best to have GC and excision jobs in place if you're handling that much volume.
Related
Have a question regarding SAN performance specifically EMC VNX SAN. I have a significant number of processes spread over number of blade servers running concurrently. The number of processes is typically around 200. Each process loads 2 small files from storage, one 3KB one 30KB. There are millions (20) of files to be processed. The processes are running on Windows Server on VMWare. The way this was originally setup was 1TB LUNs on the SAN bundled into a single 15TB drive in VMWare and then shared as a network share from one Windows instance to all the processes. The processes running concurrently and the performance is abysmal. Essentially, 200 simultaneous requests are being serviced by the SAN through Windows share at the same time and the SAN is not handling it too well. I'm looking for suggestions to improve performance.
With all performance questions, there's a degree of 'it depends'.
When you're talking about accessing a SAN, there's a chain of potential bottlenecks to unravel. First though, we need to understand what the actual problem is:
Do we have problems with throughput - e.g. sustained transfer, or latency?
It sounds like we're looking at random read IO - which is one of the hardest workloads to service, because predictive caching doesn't work.
So begin at the beginning:
What sort of underlying storage are you using?
Have you fallen into the trap of buying big SATA, configuring it RAID-6? I've seen plenty of places do this because it looks like cheap terabytes, without really doing the sums on the performance. A SATA drive starts to slow down at about 75 IO operations per second. If you've got big drives - 3TB for example - that's 25 IOPs per terabytes. As a rough rule of thumb, 200 per drive for FC/SAS and 1500 for SSD.
are you tiering?
Storage tiering is a clever trick of making a 'sandwich' out of different speeds of disk. This usually works, because usually only a small fraction of a filesystem is 'hot' - so you can put the hot part on fast disk, and the cold part on slow disk, and average performance looks better. This doesn't work for random IO or cold read accesses. Nor does it work for full disk transfers - as only 10% of it (or whatever proportion) can ever be 'fast' and everything else has to go the slow way.
What's your array level contention?
The point of SAN is that you aggregate your performance, such that each user has a higher peak and a lower average, as this reflects most workloads. (When you're working on a document, you need a burst of performance to fetch it, but then barely any until you save it again).
How are you accessing your array?
Typically SAN is accessed using a Fiber Channel network. There's a whole bunch of technical differences with 'real' networks, but they don't matter to you - but contention and bandwidth still do. With ESX in particular, I find there's a tendency to underestimate storage IO needs. (Multiple VMs using a single pair of HBAs means you get contention on the ESX server).
what sort of workload are we dealing with?
One of the other core advantages of storage arrays is caching mechanisms. They generally have very large caches and some clever algorithms to take advantage of workload patterns such as temporal locality and sequential or semi-sequential IO. Write loads are easier to handle for an array, because despite the horrible write penalty of RAID-6, write operations are under a soft time constraint (they can be queued in cache) but read operations are under a hard time constraint (the read cannot complete until the block is fetched).
This means that for true random read, you're basically not able to cache at all, which means you get worst case performance.
Is the problem definitely your array? Sounds like you've a single VM with 15TB presented, and that VM is handling the IO. That's a bottleneck right there. How many IOPs are the VM generating to the ESX server, and what's the contention like there? What's the networking like? How many other VMs are using the same ESX server and might be sources of contention? Is it a pass through LUN, or VMFS datastore with a VMDK?
So - there's a bunch of potential problems, and as such it's hard to roll it back to a single source. All I can give you is some general recommendations to getting good IO performance.
fast disks (they're expensive, but if you need the IO, you need to spend money on it).
Shortest path to storage (don't put a VM in the middle if you can possibly avoid it. For CIFS shares a NAS head may be the best approach).
Try to make your workload cacheable - I know, easier said than done. But with millions of files, if you've got a predictable fetch pattern your array will start prefetching, and it'll got a LOT faster. You may find if you start archiving the files into large 'chunks' you'll gain performance (because the array/client will fetch the whole chunk, and it'll be available for the next client).
Basically the 'lots of small random IO operations' especially on slow disks is really the worst case for storage, because none of the clever tricks for optimization work.
We would be using Virtuoso for storing RDFs, the triple count will be 100 million to start with. I need to know what should be typical RAM, CPU, Disk etc for this. Querying will be with SPARQL and there will be a bit complex queries.
Kindly provide your inputs.
The average size of a Virtuoso version 6.x triple (quad) is about 30bytes thus for 100 million triples you would need about 3GB RAM , this being the most critical component to enable the database working set to fit in memory , data does not need to be loaded from disk once the database is "warmed up", for best performance. This would be especially the case when running complex queries. In terms of disk, the fast they are the quicker the databaase can be loaded into memory, checkpoints performed etc. thus SSDs or similar devices are recommended where possible, espcially if memory is limited and reading data from disk at times in unavoidable. In terms of processor standard commodity 64bit processor available today would suffice, typically running on a Linux x86_64 system of your choice, as said memory is always the most critical component though.
See the following Virtuoso FAQ and peformance tuning documents for more details:
http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VirtRDFPerformanceTuning
http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/#FAQ
We're about to buy new hardware to run our analyses and are wondering if we're making the right decisions.
The setting:
We're a bioinformatics lab that will be handling DNA sequencing data. The biggest issue that our field has is the amount of data, rather than the compute. A single experiment will quickly go into the 10s-100s of Gb, and we would typically run different experiments at the same time. Obviously, mapreduce approaches are interesting (see also http://abhishek-tiwari.com/2010/08/mapreduce-and-hadoop-algorithms-in-bioinformatics-papers.html), but not all our software use that paradigm. Also, some software uses ascii files as in/output while other software works with binary files.
What we might be buying:
The machine that we might be buying would be a server with 32 cores and 192Gb of RAM, linked to NAS storage (>20Tb). This seems a very interesting setup for us for many of our (non-mapreduce) applications, but will such configuration prevent us from implementing hadoop/mapreduce/hdfs in a meaningful way?
Many thanks,
jan.
You have an interesting configuration. What would be the Disk IO for the NAS storage used by you?
Make your decision based on the following:
Map Reduce paradigm is used to solve the problem of handling large amount of data. Basically, RAM is more expensive than the Disk storage. You cannot hold all the data in the RAM. Disk storage allows you to store large amounts of data at cheaper costs. But, the speed at which you can read data from the disks is not very high. How does Map Reduce solve this problem? Map Reduce solves this problem by distributing the data over multiple machines. Now, the speed at which you can read data in parallel is greater than you could have done with a single storage disk. Suppose the Disk IO speed is 100 Mbps. With 100 machines you can read the data at 100*100 Mbps = 10Gbps.
Typically processor speed is not the bottleneck. Rather, the Disk IOs are the big bottlenecks while processing large amount of data.
I have a feeling that it may not be very efficient.
Note: I am aware of the question Memory management in memory intensive application, however that question appears to be about applications that make frequent memory allocations, whereas my question is about applications intentionally designed to consume as much physical memory as is safe.
I have a server application that uses large amounts of memory in order to perform caching and other optimisations (think SQL Server). The application runs on a dedicated machine, and so can (and should) consume as much memory as it wants / is able to in order to speed up and increase throughput and response times without worry of impacting other applications on the system.
The trouble is that if memory usage is underestimated, or if load increases its possible to end up with nasty failures as memory allocations fail - in this situation obviously the best thing to do is to free up memory in order to prevent the failure at the expense of performance.
Some assumptions:
The application is running on a dedicated machine
The memory requirements of the application exceed the physical memory on the machine (that is, if additional memory was available to the application it would always be able to use that memory to in some way improve response times or throughput)
The memory is effectively managed in a way such that memory fragmentation is not an issue.
The application knows what memory can be safely freed, and what memory should be freed first for the least performance impact.
The app runs on a Windows machine
My question is - how should I handle memory allocations in such an application? In particular:
How can I predict whether or not a memory allocation will fail?
Should I leave a certain amount of memory free in order to ensure that core OS operations remain responsive (and don't in that way adversely impact the applications performance), and how can I find out how much memory that is?
The core objective is to prevent failures as a result of using too much memory, while at the same time using up as much memory as possible.
I'm a C# developer, however my hope is that the basic concepts for any such app are the same regardless of the language.
In linux, the memory usage percentage is divided into following levels.
0 - 30% - no swapping
30 - 60% - swap dirty pages only
60 - 90% - swap clean pages also based on LRU policy.
90% - Invoke OOM(Out of memory) killer and kill the process consuming maximum memory.
check this - http://linux-mm.org/OOM_Killer
In think windows might have similar policy, so you can check the memory stats and make sure you never get to the max threshold.
One way to stop consuming more memory is to go to sleep and give more time for memory cleanup threads to run.
That is a very good question, and bound to be subjective as well, because the very nature of the fundamental of C# is that all memory management is done by the runtime, i.e. Garbage Collector. The Garbage Collector is a non-deterministic entity that manages and sweeps the memory for reclaiming, depending on how often the memory gets fragmented, the GC will kick in hence to know in advance is not easy thing to do.
To properly manage the memory sounds tedious but common sense applies, such as the using clause to ensure an object gets disposed. You could put in a single handler to trap the OutOfMemory Exception but that is an awkward way, since if the program has run out of memory, does the program just seize up, and bomb out, or should it wait patiently for the GC to kick in, again determining that is tricky.
The load of the system can adversely affect the GC's job, almost to a point of a Denial of Service, where everything just grind to a halt, again, since the specifications of the machine, or what is the nature of that machine's job is unknown, I cannot answer it fully, but I'll assume it has loads of RAM..
In essence, while an excellent question, I think you should not worry about it and leave it to the .NET CLR to handle the memory allocation/fragmentation as it seems to do a pretty good job.
Hope this helps,
Best regards,
Tom.
Your question reminds me of an old discussion "So what's wrong with 1975 programming ?". The architect of varnish-cache argues, that instead of telling the OS to get out of the way and manage all memory yourself, you should rather cooperate with the OS and let it understand what you intend to do with memory.
For example, instead of simply reading data from disk, you should use memory-mapped files. This allows the OS to apply its LRU algorithm to write-back data to disk when memory becomes scarce. At the same time, as long as there is enough memory, your data will stay in memory. Thus, your application may potentially use all memory, without risking getting killed.
Suppose, I wanted to develop stack overflow website. How do I estimate the amount of commodity hardware required to support this website assuming 1 million requests per day. Are there any case studies that explains the performance improvements possible in this situation?
I know I/O bottleneck is the major bottleneck in most systems. What are the possible options to improve I/O performance? Few of them I know are
caching
replication
You can improve I/O performance in several ways depending upon what you use for your storage setup:
Increase filesystem block size if your app displays good spatial locality in its I/Os or uses large files.
Use RAID 10 (striping + mirroring) for performance + redundancy (disk failure protection).
Use fast disks (Performance Wise: SSD > FC > SATA).
Segregate workloads at different times of day. e.g. Backup during night, normal app I/O during day.
Turn off atime updates in your filesystem.
Cache NFS file handles a.k.a. Haystack (Facebook), if storing data on NFS server.
Combine small files into larger chunks, a.k.a BigTable, HBase.
Avoid very large directories i.e. lots of files in the same directory (instead divide files between different directories in a hierarchy).
Use a clustered storage system (yeah not exactly commodity hardware).
Optimize/design your application for sequential disk accesses whenever possible.
Use memcached. :)
You may want to look at "Lessons Learned" section of StackOverflow Architecture.
check out this handy tool:
http://www.sizinglounge.com/
and another guide from dell:
http://www.dell.com/content/topics/global.aspx/power/en/ps3q01_graham?c=us&l=en&cs=555
if you want your own stackoverflow-like community, you can sign up with StackExchange.
you can read some case studies here:
High Scalability - How Rackspace Now Uses MapReduce and Hadoop to Query Terabytes of Data
http://highscalability.com/how-rackspace-now-uses-mapreduce-and-hadoop-query-terabytes-data
http://www.gear6.com/gear6-downloads?fid=56&dlt=case-study&ls=Veoh-Case-Study
1 million requests per day is 12/second. Stack overflow is small enough that you could (with interesting normalization and compression tricks) fit it entirely in RAM of a 64 GByte Dell PowerEdge 2970. I'm not sure where caching and replication should play a role.
If you have a problem thinking enough about normalization, a PowerEdge R900 with 256GB is available.
If you don't like a single point of failure, you can connect a few of those and just push updates over a socket (preferably on a separate network card). Even a peak load of 12K/second should not be a problem for a main-memory system.
The best way to avoid the I/O bottleneck is to not do I/O (as much as possible). That means a prevayler-like architecture with batched writes (no problem to lose a few seconds of data), basically a log file, and for replication also write them out to a socket.