Issues with storage of data in stack
iphonelagi at gmail.com
Fri Mar 23 19:41:36 CET 2018
Thanks for that - I'd forgotten I'd asked ;-)
The times you gave are super impressive - who needs mysql - Im gonna
see if I get anytime in the next month - snowed under as usual.
I think I have got it - each cluster is in effect a file on the disk?
I will understand it more when I have had time to pause the video a
few times and try to implement a very simple system although to be
honest a few thousand records should be ample in most of my stuff and
I can archive data to a history file. Sqlite does fine for my single
user systems but needing routines to change between LC dates and
sqlite dates is a pain.
On 22 March 2018 at 23:28, Mark Talluto <mark at canelasoftware.com> wrote:
> Hi Lagi,
> Sorry about the delayed reply. I have been on a long business trip. Your
> early designs are far more sophisticated than what we put together here.
> Super impressive history you have.
> LiveCode really is the champion here in that we are only using arrayEncode()
> and put myArrayA into url() to store the arrays. Selecting which array
> cluster to store might be easier to understand using a video.
> Once you understand how the array is structured, I think the method will be
> We do not preallocate space. No appending. We overwrite a cluster when one
> or more records are saved to disk. The write happens at the end of the CRUD
> operation taking place. Thus, if you ‘create’ a single record, the record is
> first created in memory and then the cluster it belongs to is written to
> disk. I have toyed with the idea of making the write to disk feature
> controllable by the dev. Thus, you could define when the write is to take
> place. For example, you might like to write to disk after every 5
> transactions or so. But, I have not found the write to affect performance in
> a noticeable way to need to add that feature.
> -Multi User-
> Yes, everything is processed sequentially in the cloud. There are no open
> sockets so you can have massive concurrent connections. All cloud calls are
> done via ‘post’. They are handled by PHP scripts to write the request to a
> cache area. One or more LiveCode standalones on the other end processes the
> request in the order they are received. Thus, should a process go down, no
> data is lost. When the process comes a back up, everything continues again
> as normal. Scale is handled by having more than one process be available.
> More scaling is handled by having data stored across multiple droplets/VMs
> (sharding). This can keep repeating itself as needed.
> -File Size Limitations-
> The OS iNode limitations are negated by not reaching its maximum limit. We
> found 40,000 files would really bring the performance down. Adding
> clustering of arrays lowers the file count to acceptable and controllable
> -Test Data-
> 100,000 records in table
> Record size average: 45 chars
> Keys in each record: last_name, first_name, date_of_birth, middle_name,
> student_number, gender, grade_level, active
> A cluster size, clusters per table, time to load all clusters from disk to
> RAM, time to write all clusters from RAM to disk, time to write one cluster
> from RAM to disk:
> 1, 16, 1.46 secs, 1.5 secs, 91.4 ms
> 2, 256, 1.52 secs, 1.5 secs, 6.7 ms
> 3, 4096, 2.38 secs, 1.6 secs, 0.8 ms
> I hope this information is helpful. Please let me know if you have any other
> Best regards,
> Mark Talluto
> On Mar 12, 2018, at 10:31 AM, Lagi Pittas <iphonelagi at gmail.com> wrote:
> Hi Mark,
> Thanks for the detailed explanation but I have a few (ish) questions ...
> Hope you don't mind me asking these questions because I did have to
> write my own access routines in those bad old days before I started on
> Clipper/Foxpro/Delphi/Btrieve and I do enjoy learning from others on
> the list and the forums - those AHA! moments when you finally get how
> the Heapsort works the night before the exam.
> Many moons ago I wrote a multi-way B-TREE based on the explanation in
> Wirth's Book "Algorithms + Data Structures = Programs" - in UCSD
> Pascal for the Apple 2, I had a 5MB hard Drives for the bigger
> companies when I was lucky, for the smaller companies I made do with 2
> 143k floppy disks and Hashing for a "large" data set- oh the memories.
> I used the B-Trees if the codes were alphanumeric. I also had my
> own method where I kept the index in the first X Blocks of the file
> and loaded the parts in memory as they were needed - a brain dead
> version of yours I suppose. I think we had about 40k of free ram to
> Play with so couldn't always keep everything in RAM. I even made the
> system multi-user and ran 20 Apple ][s on a network using a
> proprietary Nestar/Zynar network using Ribbon Cables - it worked but
> am I glad we have Ethernet!
> Anyway - I digress. I can understand the general idea of what you are
> explaining but it's the low level code for writing to the
> clusters/file on disk I'm not quite sure of.
> Which way do you build your initial file? Is it "Sparse" or prebuilt,
> or does each cluster have a "pointer" to previous or next clusters?
> Do you have records "spanning" clusters or do you leave any spare
> space in a cluster empty. Do you mark a "record" as deleted but don't
> remove the record until it's overwritten or do what Foxpro/Dbase does
> and "PACK" them with a utility routine.
> I also presume you use the "AT" option in the write command to write
> the clusters randomly since you don't wriite the whole in memory table
> Which brings me onto my final questions - I presume your system is
> multi-user because you have a server program that receives calls and
> executes them sequentially? And lastly what are the file size
> limitations doing it this way - do You also virtualize the data in
> Sorry for all the question but this is the interesting stuff
> Regards Lagi
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