I am very new to Redis. I've implemented caching in our application and it works nicely. I want to store two main data types: a directory listing and file content. It's not really relevant, but this will cache files served up via WebDAV.
I want the file structure to remain almost forever. The file content needs to be cached for a short time only. I have set up my expiry/TTL to reflect this.
When the server reaches memory capacity is it possible to priorities certain cached items over others? i.e. flush a key, flush a whole database or flush a whole instance of Redis.
I want to keep my directory listing and flush the file content when memory begins to be an issue.
EDIT: Reading this article seems to be what I need. I think I will need to use volatile-ttl. My file content will have a much shorter TTL set, so this should in theory clear that first. If anyone has any other helpful advice I would love to hear it, but for now I am going to implement this.
Reading this article describes what I needed. I have implemented volatile-ttl as my memory management type.
Related
Let's say I am sending log entries to ElasticSearch. We are considering adding the calling method, calling class, and line of code to our log entries. Being that these fields will contain similar values, would ElasticSearch attempt to preserve disk space by not copying this data for every occasion of the same value?
EDIT - Additional clarification: I did not read anywhere that Elastic does this. I know that some data storage systems, like columnar databases, write their data to disk so as to preserve disk storage by not writing duplicated data over and over again. So I am wondering if ElasticSearch implements similar techniques..
As far as I know: no, it doesn't. It would make several key features quite hard I believe, and I have not seen any reference to this practice.
It's tricky to 'proof' the non-existence of some method unless you look at all source-code, but I would expect this page about disk usage tuning to containt references to this practice.
Did you read anywhere about this, or does it just seem practical to you?
I realize that this might be a vague question the bequests a vague answer, but I'm in need of some real world examples, thoughts, &/or best practices for caching data for a web app. All of the examples I've read are more technical in nature (how to add or remove cache data from the respective cache store), but I've not been able to find a higher level strategy for caching.
For example, my web app has an inbox/mail feature for each user. What I've been doing to date is storing typical session data in the cache. In this example, when the user logs in I go to the database and retrieve the user's mail messages and store them in cache. I'm beginning to wonder if I should just maintain a copy of all users' messages in the cache, all the time, and just retrieve them from cache when needed, instead of loading from the database upon login. I have a bunch of other data that's loaded on login (product catalogs and related entities) and login is starting to slow down.
So I guess my question to the community, is what would you do/recommend as an approach in this scenario?
Thanks.
This might be better suited to https://softwareengineering.stackexchange.com/, but generally you want to cache:
Metadata/configuration data that does not change frequently. E.g. country/state lists, external resource addresses, logic/branching settings, product/price/tax definitions, etc.
Data that is costly to retrieve or generate and that does not need to frequently change. E.g. historical data sets for reports.
Data that is unique to the current user's session.
The last item above is where you need to be careful as you can drastically increase your app's memory usage, by adding a few megabytes to the data for every active session. It also implies different levels of caching -- application wide, user session, etc.
Generally you should NOT cache data that is under active change.
In larger systems you also need to think about where the cache(s) will sit. Is it possible to have one central cache server, or is it good enough for each server/process to handle its own caching?
Also: you should have some method to quickly reset/invalidate the cached data. For a smaller or less mission-critical app, this could be as simple as restarting the web server. For the large system that I work on, we use a 12 hour absolute expiration window for most cached data, but we have a way of forcing immediate expiration if we need it.
This is a really broad question, and the answer depends heavily on the specific application/system you are building. I don't know enough about your specific scenario to say if you should cache all the users' messages, but instinctively it seems like a bad idea since you would seem to be effectively caching your entire data set. This could lead to problems if new messages come in or get deleted. Would you then update them in the cache? Would that not simply duplicate the backing store?
Caching is only a performance optimization technique, and as with any optimization, measure first before making substantial changes, to avoid wasting time optimizing the wrong thing. Maybe you don't need much caching, and it would only complicate your app. Maybe the data you are thinking of caching can be retrieved in a faster way, or less of it can be retrieved at once.
Cache anything that causes duplicate database queries.
Client side file caching is important as well. Assuming files are marked with an id in your database, cache them on every network request to avoid many network requests for the same file. A resource to do this can be found here (https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API). If you don't need to cache files, web storage, local storage and cookies are good for smaller pieces of data.
//if file is in cache
//refer to cache
//else
//make network request and push file to cache
I'm currently using MySql to store my sessions. It works great, but it is a bit slow.
I've been asked to use Redis, but I'm wondering if it is a good idea because I've heard that Redis delays write operations. I'm a bit afraid because sessions need to be real-time.
Has anyone experienced such problems?
Redis is perfect for storing sessions. All operations are performed in memory, and so reads and writes will be fast.
The second aspect is persistence of session state. Redis gives you a lot of flexibility in how you want to persist session state to your hard-disk. You can go through http://redis.io/topics/persistence to learn more, but at a high level, here are your options -
If you cannot afford losing any sessions, set appendfsync always in your configuration file. With this, Redis guarantees that any write operations are saved to the disk. The disadvantage is that write operations will be slower.
If you are okay with losing about 1s worth of data, use appendfsync everysec. This will give great performance with reasonable data guarantees
This question is really about real-time sessions, and seems to have arisen partly due to a misunderstanding of the phrase 'delayed write operations' While the details were eventually teased out in the comments, I just wanted to make it super-duper clear...
You will have no problems implementing real-time sessions.
Redis is an in-memory key-value store with optional persistence to disk. 'Delayed write operations' refers to writes to disk, not the database in general, which exists in memory. If you SET a key/value pair, you can GET it immediately (i.e in real-time). The policy you select with regards to persistence (how much you delay the writes) will determine the upper-bound for how much data could be lost in a crash.
Basically there are two main types available: async snapsnots and fsync(). They're called RDB and AOF respectively. More on persistence modes on the official page.
The signal handling of the daemonized process syncs to disk when it receives a SIGTERM for instance, so the data will still be there after a reboot. I think the daemon or the OS has to crash before you'll see an integrity corruption, even with the default settings (RDB snapshots).
The AOF setting uses an Append Only File that logs the commands the server receives, and recreates the DB from scratch on cold start, from the saved file. The default disk-sync policy is to flush once every second (IIRC) but can be set to lock and write on every command.
Using both the snapshots and the incremental log seems to offer both a long term don't-mind-if-I-miss-a-few-seconds-of-data approach with a more secure, but costly incremental log. Redis supports clustering out of the box, so replication can be done too it seems.
I'm using the default RDB setting myself and saving the snapshots to remote FTP. I haven't seen a failure that's caused a data loss yet. Acute hardware failure or power outages would most likely, but I'm hosted on a VPS. Slim chance of that happening :)
I am running ATG 9 with a bunch of different objects configured in the repository.xml to have specific cache sizes, ttl etc.
For example:
<item-descriptor name="USER"
query-expire-timeout="300000"
item-expire-timeout="300000"
item-cache-timeout="300000"
item-cache-size="20000"
query-cache-size="50">
...
I am expecting that the cache would not grow above that size and would expire old items to keep the cache size at or under the item-cache-size. However when I look at the cache stats in the Dynamo admin console, I see several of our items have usedRatios of 500-1000%. This is hogging all of the memory in the JVM over time as more and more items are cached and apparently never released. If I invoke the invalidateCaches method on the Repository in the admin console the free memory jumps back way up and then the slow march down begins again.
How can I ensure that the caches do not grow over their configured size and take over all the memory? Is there some configuration setting I am missing? Are there code tricks one must employ to keep the cache from growing out of control? The ATG docs aren't the most informative and googling around hasn't yielded much info either.
After starting your ATG instance, I suggest navigating to the ProfileAdapterRepository in the Dynamo Admin page (/dyn/admin/nucleus/atg/userprofiling/ProfileAdapterRepository/?propertyName=definitionFiles) and viewing the combined view of the repository definition files. It's the best way to be sure what the final file looks like, since it can be built up of many files.
You should see the attributes you've configured on the "user" repository item through this interface (Note all lower case).
If you don't see your attributes here then you probably don't have your repository definition file loaded, either the module you're working on isn't started or the file is not on the configuration path.
Im trying to find a good way to handle memcache keys for storing, retrieving and updating data to/from the cache layer in a more civilized way.
Found this pattern, which looks great, but how do I turn it into a functional part of a PHP application?
The Identity Map pattern: http://martinfowler.com/eaaCatalog/identityMap.html
Thanks!
Update: I have been told about the modified memcache (memcache-tag) that apparently does do a lot of this, but I can't install linux software on my windows development box...
Well, memcache use IS an identity map pattern. You check your cache, then you hit your database (or whatever else you're using). You can go about finding information about the source by storing objects instead of just values, but you'll take a performance hit for that.
You effectively cannot ask the cache what it contains as a list. To mass invalidate, you'll have to keep a list of what you put in and iterate it, or you'll have to iterate every possible key that could fit the pattern of concern. The resource you point out, memcache-tag can simplify this, but it doesn't appear to be maintained inline with the memcache project.
So your options now are iterative deletes, or totally flushing everything that is cached. Thus, I propose a design consideration is the question that you should be asking. In order to get a useful answer for you, I query thus: why do you want to do this?