I am looking at using a spell checker for my GAE app and we have an algorithm already for spell checking, but I'm trying to figure out how to best store and load dictionary files for best performance.
I am considering the following strategies:
Place the dictionary data in a text file(s) in local app engine storage and load/read them using standard IO methods (open(),read(),etc)
Place the dictionary data in GCS and load/read using GCS IO methods
Place the dictionary data in an ndb.model() and load/cache information
One cache I don't quite understand is the context cache -- is this cache that is attached to a given instance? I.e. if I have a resident instance that is spun up, can I go ahead and load the dictionary data into the instance's RAM and thus accessing data should be extremely fast (microsecond vs millisecond seek/get times)? The dictionary data will probably be a sharded list of some sort that we'll optimize for performance. Are there other data storage methods/structures I'm not considering here that may be more appropriate? Thanks.
Cache (or its full name memcache) isn't exactly RAM but similar. When used with NDB it acts like a buffer. When you do writes it writes to the Memcache first then to the DB. Though this may sound slower its not, as writes to the DB take a while before they are accessible. When it reads it checks memcache, if it exists then it uses that info otherwise it pulls from the DB, stores it in Memcache then gives you the data. Just like RAM though its volatile, thus you cannot guaranty information is always acceptable, its limited (depending on what type of instance you have) and can be flush with no warning or reason. You can read more here:
https://developers.google.com/appengine/docs/python/memcache/
https://developers.google.com/appengine/articles/scaling/memcache
Ultimately Memcahe will be the fastest and most accessible as it it shared amongst all your instances, so if one instance pulls some data from the datastore then all of them can access it quickly. Even if its not in memcache it is still the fastest of all the options, as the others ones will fill up your memory and may cause errors and performance issues.
Related
I am writing a Web crawler for Linked Data and I need to store crawled URIs on the disk (not necessarily distributed but could be). My crawler will check if a URI exists in the storage constantly. If a URI does exists, it will do nothing, if it does not exists, it will crawl the URI and write the URI to the storage. At first, since the storage will be rather empty, there will be more writes than reads but at some point, reads will be more than writes and I favor faster reads. I don't need any join operations etc.
I am thinking about a document based NoSQL storage and I define a key="domain of a URI", value="an array of the whole URIs". I am not sure if I need a secondary index on the value.
Since you are only interested in searching and storing, SQLite is suitable for your purposes. It's a lightweight database engine.
Selamlar.
I am writing a small appengine application and I want to start using Datastore.
My app has some users and each user is a complicated JAVA class.
Users might swap some "point" objects between them, so I need the data to be available and fast.
My question is quite generic:
How should I handle the data caching?
Storing the data entirely in the Datastore and fetching it in every call sounds slow in runtime.
On the other hand, holding the data in a static JAVA class sounds tricky, because every now and then the server resets and data is erased.
If I had a main loop, like in a regular console application I would've probably saved the data twice-three times a day on predefined hours of the day.
How should I manage my code in such a way that it would save the status in the datastore every now and then and this way will not loose any data.
if the number of write operations are less than read operations,
it is good to try "Write-through caching" with memcache module.
https://developers.google.com/appengine/docs/java/memcache/
basically the write-through caching works like this:
for every write operations, program update the datastore, then backup the data to memcache
for every read operations, program will try to read from memcache. if it can found required data, then it returns, otherwise it will fetch the data from datastore and backup the results to memcache.
As #lucemia noted, you should use memcache.
If you use objectify, you can set-up caching declaratively, without writing and cache handling code.
I'm building a GAE app that requires a cryptographic key to operate. I would like to avoid storing the key in code or in a persistent datastore, and instead upload the key whenever I start my app so that it will only reside in memory for the duration of the app's lifetime (from the time I upload the key until no instances are running.)
I understand that this is possible to do with a resident backend, but this seems too expensive (cheapest backend is currently 58$/month) just to keep one value in memory and serve it to other instances on demand.
Note that I'm not looking for a general robust shared-memory solution, just one value that is basically written once and read many times. Thanks.
I don't think that this can work the way you hope. The sources of data in GAE:
Files deployed with your app (war or whatever).
Per-instance memory (front-end or back-end).
Memcache.
Datastore (or SQL now, I suppose).
Blobstore.
Information retrieved via http requests (i.e. store it somewhere else).
1 and 4 are out, as per your question. 2 doesn't work by itself because the starting and stopping of instances is out of your control (it wouldn't scale otherwise), and persistent instances are expensive. 3 doesn't work by itself because Memcache can be cleared at any time. 5 is really no different than the datastore, as it is permanently stored on the Google's servers. Maybe you could try 6 (store it somewhere else), and retrieve it into per-instance memory during the instance startup. But I suspect that is no better security-wise (and, for that matter, doesn't match with what you said that you wanted).
It seems that a Memcache and local memory solution might work if you:
have your server instances clear the memcached key on exit and
existing server instances write/refresh the key regularly (for
example on every request).
That way the key will likely be there as long as an instance is operational and most likely not be there when starting up cold.
The same mechanism could also be used to propagate a new key and/or cycle server instances in the event of a key change.
I have a very small amount of data (~200 bytes) that I retrieve from the database very often. The write rate is insignificant.
I would like to get away from all the unnecessary database calls to fetch this almost static data. Is memcached a good use for this? Something else?
If it's of any relevance I'm running this on GAE using python. The data in question could be easily (de)serialized as json.
Memcache is well-suited for this - reading from the datastore is much more expensive than reading from memcache. This is especially true for small amounts of data for which the cost to retrieve is dominated by latency to the datastore.
If your app receives enough requests that instances typically stay alive for a little while, then you could go one step further and use App Caching to largely avoid memcache too. (Basically, cache the value in a global variable, and also app-cache the time the value was last updated. Provide an accessor for the value which retrieves the latest from memcache/db if it hasn't been updated in X minutes). Memcache is pretty cheap though, so this extra work might only make sense if you access this variable rather frequently.
If it changes less often than once per day, you could just hardcode it in webapp code, and reupload the file each time it changes.
First of all I'll explain the question. By persistence, I mean storing data beyond the execution of a single request. It might not be the best question title, so feel free to edit it.
The way I see it, there are three types of persistence in GAE, each one "closer" to the request itself:
The datastore
This is where all data is most likely to be based. It may go into the higher layers of persistence temporarily, but in the end, this is where the data really is. Unfortunately, querying the datastore repeatedly is slow and uses a lot of resources.
Use when...
storing data that should be stored for an indefinite amount of time.
Avoid using when...
getting data that is queried often but rarely updated.
memcache
This is a highly complex caching engine that stores the data in memory and makes sure all users read from/write to the same cache. It's a much faster way to get/set data on a key→value basis than using the datastore. Unfortunately, data can only stay in the memory for so long, and there is no guarantee that it will stay for as long as you tell it to; the data may disappear at any time if memory is needed elsewhere.
Use when...
you need to get data more often than you need to update it. Even when data needs to be updated often, it can have its uses (if a few missed updates are considered okay), by setting up a task queue to persist data from the memcache to the datastore.
Avoid using when...
data needs to be updated often and has to be up-to-date when fetched.
Global variables
This isn't an official method of persisting data, but it works. However, it's the least reliable method, and since it has no data synchronization across servers, persisted data may show up differently for different users (but from what I've found, the server rarely changes for the same user.) Theoretically, this should be the method that has the least overhead in getting/setting values, however, and could have its uses.
Use when...
hell freezes over? I don't know... I haven't enough knowledge about what goes on behind the scenes to actually rely on this method. Discuss!
Avoid using when...
you rely on the data being the same across servers.
Cookies
If the data is user-specific, it can be efficient to store it as a cookie in the user's browser. There are some pitfalls to watch out for though:
Security – the user can meddle with cookies, and malicious people could potentially do the same. To make sure that the contents are unreadable and unchangeable to all, the cookie can be encrypted using the PyCrypto library which is available on GAE.
Performance – since cookies are sent with every request (even images), it can add to the bandwidth being used, and slow down requests. One solution is to use another domain for static content, so the browser won't send the cookie for that content.
When should the different types of persistence be used? How can they be combined to reduce/even out the amount of resources being spent?
Datastore
Use the datastore to hold any long living information. The datastore should be used like you would use a normal database to hold data that will be used in your site/application.
MemCache
Use this to access data a lot quicker than trying to access the datastore. MemCache can return data really quickly and can be used for any data that needs to span multiple calls from users. It is normally data that was originally in the datastore and then moved to the memcache.
def get_data():
data = memcache.get("key")
if data is not None:
return data
else:
data = self.query_for_data() #get data from the datastore
memcache.add("key", data, 60)
return data
The memcache will flush itself when the item is out of date. You set this in the last param of the add shown above.
Global Variables
I wouldn't use these at all since they can't span instances. In GAE a request creates a new instance, well in python it does. If you want to use Global variables I would store the data needed in the memcache.
Your post is a good summary of the 3 major options. You mostly have answered the question already. However, if you are currently building an app and stressing over whether or not you should memcache something, try this:
Write your app using the datastore for everything that needs to outlive more than one request.
Once your app (or some usable subset) is working, run some functional tests or simulations to see where the slow spots (or high quota usage) are.
Find the most slow or inefficient request path, and figure out how to make that faster (either by using memcache, or altering your datastructures so you can do gets instead of queries, or possibly storing something in a global instance variable*)
goto 2 until you're satisfied.
*Things that might be good for a "global" variable would be something that is relatively expensive to create/fetch, that a substantial portion of your requests will use, and that does not need to be consistent across requests/users.
I use global variable to speed up json conversion. Before I convert my data structure to json, I hash it and check if the json if already available. For my app this gives quite a speedup as the pure python implementation is quite slow.
Global variables
To complement AutomatedTester's answer, and also reply his further question about how to share information between GETs without memcache or datastore, below a quick illustration of how to use global variables:
if 'i' not in globals():
i = 0
def main():
global i
i += 1
print 'Status: 200'
print 'Content-type: text/plain\n'
print i
if __name__ == '__main__':
main()
Calling this script multiple times will give you 1, 2, 3... Of course as mentioned earlier by Blixt you should not count on this trick too much ('i' can sometimes switch back to zero) but it can be useful to store user-specific information in a dictionary, session data for instance.