I am writing an application that refreshes indexedDB storage on each reload and generates a new set of key-value pairs on the database. The key value gets incremented each time since I am using objectStore.clear() before writing new key-value pairs.
What is the maximum value I can get to?
Is there a way to reset it to start from 1 again?
I know deleting the object store is one approach for this but the application is a time-sensitive operation and deleting takes some time to complete.
The specification for this is at: https://w3c.github.io/IndexedDB/#key-generator-construct. The maximum value is 2^53 (9007199254740992). As the spec notes:
As long as key generators are used in a normal fashion this limit will not be a problem. If you generate a new key 1000 times per second day and night, you won’t run into this limit for over 285000 years.
There is no way to reset the key generator for an object store.
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Summary
What is the "best practice" way to store large JSON arrays on a remote web service?
Background
I've got a service, "service A", that generates JSON objects, an "item", no larger than 1KiB. Every time it emits an item, the item needs to be appended to a JSON array. Later, a user can get all these arrays of items, which can be 10s of MiB or more.
Performance
What is the best way to store JSON to make appending and retrieval performant? Ideally, insertation would be O(1) and retrieval would be fast enough that we didn't need to tell the user to wait until their files have downloaded.
The downloads have never become so large that the constraint is the time to download them from the server (if they were a 10 MiB file). The constraint has always been the time to compute the file.
Stack
Our current stack is running Django + Postgresql on Elasticbeanstalk. New services are acceptable (e.g. S3 if append were supported).
Attempted Solutions
When we try to store all JSON in a single row in the database, performance is understandably slow.
When we try to store each JSON object in a separate row, it takes too long to aggregate the separate rows into a single array of items. In addition, a user requests all item arrays in their account every time they visit the main screen of the app, so it is inefficient to recompute the aggregated array of items each time.
Our API is designed to generate UUIDs in MySQL for all records.
However, 99% of the records being generated in all tables share the same last 3 blocks of the UUID. I'm assuming this is because MySQL uses v1 of UUID which is based on Mac address which doesn't change on the same server. It doesn't seem like enough entropy to have a high level of confidence in uniqueness.
e.g. XXXXXXXX-XXXX-46fc-bb08-f9b12276ed01
This is validated per Wikipedia:
"given the speed of modern processors, successive invocations on the same machine of a naive implementation of a generator of version 1 UUIDs may produce the same UUID, violating the uniqueness property. (Non-naïve implementations can avoid this problem by, for example, remembering the most recently generated UUID, "pocketing" unused UUIDs, and using pocketed UUIDs in case a duplicate is about to be generated.)"
It sounds like if enough API calls are made within a certain amount of time that collision would all be certain (just a matter of reaching transactional volume e.g. 1000 transactions a second? i.e. close to 1 transaction per millisecond).
Assumption: UUID() is function of the MySQL binary which cannot be changed.
At what volume do I need to evaluate a change to prevent collisions and how would I make the wikipedia recommended change in MySQL to "pocket" UUIDs?
Put a unique constraint on your UUID column. That'll make the database check for duplicates before inserting (or updating) a record, so you can be sure there are no collisions in the table. The colliding record will just fail to insert.
If you find that you're actually getting errors due to violation of that constraint — i.e. if collisions are actually happening in the UUID generator and the database is keeping them out of the table — then you can look into more sophisticated methods to generate a new UUID and try again. But chances are, you won't have any problems.
The timestamp field in the UUID is measured in 100-nanosecond intervals, so you'd have to generate two UUIDs within a tenth of a microsecond to get a collision. That corresponds to a rate of ten million transactions per second. A thousand should be fine.
I'm implementing a sequence generator for my database, via Grails. I've defined my domain class, and I want to specify a sequence. At present, I'm using:
static mapping = {
id generator: 'uuid'
version false
}
But this is generating long, 128 bit IDs, which I'm conscious that the user might have a hard time using. To combat this, I decided that it might be better to use normal, incrementing IDs, so I found this resource, informing me about the various options I had for preconfigured sequence generators.
I had a look at increment, and found this description:
increment -
generates identifiers of type long, short or int that are unique only when no other process is inserting data into the same table. Do not use in a cluster.
I have one Grails application inserting data, but several users may be inputting data at the same time. As I understand it, Grails (like a normal Servlet) will assign a new thread to each request made from the user. Does this mean then, that increment is not a good fit, because even though there is only one application, there will be multiple threads attempting to insert?
If increment is not a good fit, what other options do I have?
If increment is not a good fit, what other options do I have?
As IsidroGHIf stated in the comment if you don't specify id generator GORM will by default choose the native strategy to generate ids, which in case of MySQL are auto-incrementing columns.
They are definitely thread-safe, they also work in a cluster (compared to increment) and they will by default start with 1, incrementing also by 1 so it won't be long 128 bit IDs.
All threads running in a single instance application are executed in the same process so using increment is thread safe.
In this case, the Hibernate session associated to the process (associated to a thread running in the process) manages these IDs and assures uniqueness.
Anyway, the increment mode is normally (at least in my case) used for testing and/or early stage development phases.
If your database supports sequences, I'd probably use the sequence mode.
I have a very high throughput site for which I'm trying to store "view counts" for each page in a mySQL database (for legacy reasons they must ultimately end up in mySQL).
The sheer number of views is making it impractical to do SQL "UPDATE ITEM SET VIEW_COUNT=VIEW_COUNT+1" type of statements. There are millions of items but most are only viewed a small number of times, others are viewed many times.
So I'm considering using Redis to gather the view counts, with a background thread that writes the counts to mySQL. What is the recommended method for doing this? There are some issues with the approach:
how often does the background thread run?
how does it determine what to write back to mySQL?
should I store a Redis KEY for every ITEM that gets hit?
what TTL should I use?
is there already some pre-built solution or powerpoint presentation that gets me halfway there, etc.
I have seen very similar questions on StackOverflow but none with a great answer...yet! Hoping there's more Redis knowledge out there at this point.
I think you need to step back and look at some of your questions from a different angle to get to your answers.
"how often does the background thread run?"
To answer this you need to answer these questions: How much data can you lose? What is the reason for the data being in MySQL, and how often is that data accessed? For example, if the DB is only needed to be consulted once per day for a report, you might only need it to be updated once per day. On the other hand, what if the Redis instance dies? How many increments can you lose and still be "ok"? These will provide the answers to the question of how often to update your MySQL instance and aren't something we can answer for you.
I would use a very different strategy for storing this in redis. For the sake of the discussion let us assume you decide you need to "flush to db" every hour.
Store each hit in hashes with a key name structure along these lines:
interval_counter:DD:HH
interval_counter:total
Use the page id (such as MD5 sum of the URI, the URI itself, or whatever ID you currently use) as the hash key and do two increments on a page view; one for each hash. This provides you with a current total for each page and a subset of pages to be updated.
You would then have your cron job run a minute or so after the start of the hour to pull down all pages with updated view counts by grabbing the previous hour's hash. This provides you with a very fast means of getting the data to update the MySQL DB with while avoiding any need to do math or play tricks with timestamps etc.. By pulling data from a key which is no longer bing incremented you avoid race conditions due to clock skew.
You could set an expiration on the daily key, but I'd rather use the cron job to delete it when it has successfully updated the DB. This means your data is still there if the cron job fails or fails to be executed. It also provides the front-end with a full set of known hit counter data via keys that do not change. If you wanted, you could even keep the daily data around to be able to do window views of how popular a page is. For example if you kept the daily hash around for 7 days by setting an expire via the cron job instead of a delete, you could display how much traffic each page has had per day for the last week.
Executing two hincr operations can be done either solo or pipelined still performs quite well and is more efficient than doing calculations and munging data in code.
Now for the question of expiring the low traffic pages vs memory use. First, your data set doesn't sound like one which will require huge amounts of memory. Of course, much of that depends on how you identify each page. If you have a numerical ID the memory requirements will be rather small. If you still wind up with too much memory, you can tune it via the config, and if needs be could even use a 32 bit compile of redis for a significant memory use reduction. For example, the data I describe in this answer I used to manage for one of the ten busiest forums on the Internet and it consumed less than 3GB of data. I also stored the counters in far more "temporal window" keys than I am describing here.
That said, in this use case Redis is the cache. If you are still using too much memory after the above options you could set an expiration on keys and add an expire command to each ht. More specifically, if you follow the above pattern you will be doing the following per hit:
hincr -> total
hincr -> daily
expire -> total
This lets you keep anything that is actively used fresh by extending it's expiration every time it is accessed. Of course, to do this you'd need to wrap your display call to catch the null answer for hget on the totals hash and populate it from the MySQL DB, then increment. You could even do both as an increment. This would preserve the above structure and would likely be the same codebase needed to update the Redis server from the MySQL Db if you the Redis node needed repopulation. For that you'll need to consider and decide which data source will be considered authoritative.
You can tune the cron job's performance by modifying your interval in accordance with the parameters of data integrity you determine from the earlier questions. To get a faster running cron nob you decrease the window. With this method decreasing the window means you should have a smaller collection of pages to update. A big advantage here is you don't need to figure out what keys you need to update and then go fetch them. you can do an hgetall and iterate over the hash's keys to do updates. This also saves many round trips by retrieving all the data at once. In either case if you will likely want to consider a second Redis instance slaved to the first to do your reads from. You would still do deletes against the master but those operations are much quicker and less likely to introduce delays in your write-heavy instance.
If you need disk persistence of the Redis DB, then certainly put that on a slave instance. Otherwise if you do have a lot of data being changed often your RDB dumps will be constantly running.
I hope that helps. There are no "canned" answers because to use Redis properly you need to think first about how you will access the data, and that differs greatly from user to user and project to project. Here I based the route taken on this description: two consumers accessing the data, one to display only and the other to determine updating another datasource.
Consolidation of my other answer:
Define a time-interval in which the transfer from redis to mysql should happen, i.e. minute, hour or day. Define it in a way so that fast and easyly an identifying key can be obtained. This key must be ordered, i.e. a smaller time should give a smaller key.
Let it be hourly and the key be YYYYMMDD_HH for readability.
Define a prefix like "hitcount_".
Then for every time-interval you set a hash hitcount_<timekey> in redis which contains all requested items of that interval in the form ITEM => count.
There exists two parts of the solution:
The actual page that has to count:
a) get the current $timekey, i.e. by date- functions
b) get the value of $ITEM
b) send the redis-command HINCRBY hitcount_$timekey $ITEM 1
A cronjob which runs in that given interval, not too close to the limit of that intervals (in example: not at the full hour). This cronjob:
a) Extracts the current time-key (for now it would be 20130527_08)
b) Requests all matching keys from redis with KEYS hitcount_* (those should be a small number)
c) compares every such hash against the current hitcount_<timekey>
d) if that key is smaller than current key, then process it as $processing_key:
read all pairs ITEM => counter by HGETALL $processing_key as $item, $cnt
update the database with `UPDATE ITEM SET VIEW_COUNT=VIEW_COUNT+$cnt where ITEM=$item"
delete that key from the hash by HDEL $processing_key $item
no need to del the hash itself - there are no empty hashes in redis as far as I tried
If you want to have a TTL involved, say if the cleanup-cronjob may be not reliable (as might not run for many hours), then you could create the future hashes by the cronjob with an appropriate TTL, that means for now we could create a hash 20130527_09 with ttl 10 hours, 20130527_10 with TTL 11 hours, 20130527_11 with TTL 12 hours. Problem is that you would need a pseudokey, because empty hashes seem to be deleted automatically.
See EDIT3 for current state of the A...nswer.
I would write a key for every ITEM. A few tenthousand keys are definitely no problem at all.
Do the pages change very much? I mean do you get a lot of pages that will never be called again? Otherwise I would simply:
add the value for an ITEM on page request.
every minute or 5 minutes call a cronjob that reads the redis-keys, read the value (say 7) and reduce it by decrby ITEM 7. In MySQL you could increment the value for that ITEM by 7.
If you have a lot of pages/ITEMS which will never be called again you could make a cleanup-job once a day to delete keys with value 0. This should be locked against incrementing that key again from the website.
I would set no TTL at all, so the values should live forever. You could check the memory usage, but I see a lot of different possible pages with current GB of memory.
EDIT: incr is very nice for that, because it sets the key if not set before.
EDIT2: Given the large amount of different pages, instead of the slow "keys *" command you could use HASHES with incrby (http://redis.io/commands/hincrby). Still I am not sure if HGETALL is much faster then KEYS *, and a HASH does not allow a TTL for single keys.
EDIT3: Oh well, sometimes the good ideas come late. It is so simple: Just prefix the key with a timeslot (say day-hour) or make a HASH with name "requests_". Then no overlapping of delete and increment may happen! Every hour you take the possible keys with older "day_hour_*" - values, update the MySQL and delete those old keys. The only condition is that your servers are not too different on their clock, so use UTC and synchronized servers, and don't start the cron at x:01 but x:20 or so.
That means: a called page converts a call of ITEM1 at 23:37, May 26 2013 to Hash 20130526_23, ITEM1. HINCRBY count_20130526_23 ITEM1 1
One hour later the list of keys count_* is checked, and all up to count_20130523 are processed (read key-value by hgetall, update mysql), and deleted one by one after processing (hdel). After finishing that you check if hlen is 0 and del count_...
So you only have a small amount of keys (one per unprocessed hour), that makes keys count_* fast, and then process the actions of that hour. You can give a TTL of a few hours, if your cron is delayed or timejumped or down for a while or something like that.
I have a photo-hosting website, and I want to keep track of views to the photos. Due to the large volume of traffic I get, incrementing a column in MySQL on every hit incurs too much overhead.
I currently have a system implemented using Memcache, but it's pretty much just a hack.
Every time a photo is viewed, I increment its photo-hits_uuid key in Memcache. In addition, I add a row containing the uuid to an invalidation array also stored in Memcache. Every so often I fetch the invalidation array, and then cycle through the rows in it, pushing the photo hits to MySQL and decrementing their Memcache keys.
This approach works and is significantly faster than directly using MySQL, but is there a better way?
I did some research and it looks like Redis might be my solution. It seems like it's essentially Memcache with more functionality - the most valuable to me is listing, which pretty much solves my problem.
There is a way that I use.
Method 1: (Size of a file)
Every time that someone hits the page, I add one more byte to a file. Then after x seconds or so (I set 600), I will count how many bytes that are in my file, delete my file, then I update it to the MySQL database. This will also allow scalability if multiple servers are adding to a small file in a cache server. Use fwrite to append to the file and you will never have to read that cache file.
Method 2: (Number stored in a file)
Another method is to store a number in a text file that contains the number of hits, but I recommend from using this because if two processes were simultaneously updating, data might be off (maybe same with method1).
I would use method 1 because although it is a bigger file size, it is faster.
I'm assuming you're keeping access logs on your server for this solution.
Keep track of the last time you checked your logs.
Every n seconds or so (where n is less than the time it takes for your logs to be rotated, if they are), scan through the latest log file, ignoring every hit until you find a timestamp after your last check time.
Count how many times each image was accessed.
Add each count to the count stored in the database.
Store the timestamp of the last log entry you processed for next time.