I want to use CollectD to gather some statistics (about storage) and have Graphite display them nicely. Apparently this can be done either by
having CollectD store the data as RRD files and pointing Graphite at
those, or
using a CollectD plugin to push the data to Graphite's Carbon API, which will store the data in a Whisper database (which is similar to RRD but not compatible).
I think I want to go with RRDs, but I found this statement in the Whisper docs that concerns me:
In many cases (depending on configuration) if an update is made to an
RRD series but is not followed up by another update soon, the original
update will be lost.
Hmmm. That's a bit scary, but the accusation is so vague that I don't know what to make of it. What is the configuration they are talking about, and the situation in which it causes data loss?
My situation is that the metrics data I am gathering will be available in chunks -- periodically I will go get the latest data and make as many entries into the database as there are new samples available. So, for example, I might grab some data and update the database with the values from 3 minutes ago, 2 minutes ago, and 1 minute ago, one right after the other. In fact, I might have dozens of new samples to put in the database at once. Does using RRD this way have anything to do with the Whisper accusation?
NOTE: I do not need to back-fill data; I will always be adding newer data than what has already been stored.
One scenario I see this happening would be if you have an AVERAGE RRA setup, and have the xxf value set to a low percentage. When the data is compressed over time, you could receive an unknown value and 'loose' all the data that was averaged. If you are using a RRD for what it was designed for, and have it setup with the proper type and settings, I wouldn't think you will run into a problem.
I would recommend taking an in depth look at the RRD documentation found HERE to answer questions about how RRD's and RRA's handle the data, and the different storage techniques that are available to you.
Related
Autosar Diagnostic is implemented by taking standards of UDS ( ISO 14229).
As per that, once DTC is logged the snap shot data is stored as per UDS. Snap shot data is implemented via freeze frame data concept in Autosar Dem Module.
But I want to save some more information about DTC apart form snap shot data. I want to store data to be stored before 3 second and after 1 second of confirming DTC with sampling of 400 millisecond. So I need to store 10 samples of data every time when DTC gets locked.
I want to implement this time domain data in Autosar Diagnostic. Can I do that?, If yes, How?
Thanks.
We had a customer, that wanted to have almost the same, 15 FreezeFrames, 12 before the failure, one at the failure, and two after that, with a similar cyle. We used a ringbuffer updated cyclically. We used a callout from Dem (either DemCallbackEventStatusChanged() or DemCallbackDTCStatusChanged()), to stop the ringbuffer and count for two more. After they are logged, we stored them in an extra NvM Block. You might have several of these NvM blocks, and link that number to the DemEvent (FF Data?). E.g. the NvM Block could be a NVM_DATASET, so you could use an index. When reading out DTCs look for the assignment and read out the NvM DataSet index.
Otherwise, you might find a way with StorageConditions, disable them at first at first reporting and enabling after the freezeframes are complete?
I don't know though of a Dem feature to support this directly.
I don't really understand where your problem is.
As you mentioned, snapshot data is stored together with the DTC. The content of the snapshot data you can define referencing DIDs. So, you need to define a new (internal) DID (in Dcm) where you provide your time domain data and add this DID in the Dem to the snapshot data (freeze frame).
I have the following use case for which I'm trying to find an optimal use of either filsystem, database (rdbms or a flavour of noSql solution). Any advice is welcome, as I want to see what is optimal.
Client application: will generate logs intervals of 1-3 seconds. By logs I mean structured log data (either about connections, applications used, processes used, screenshots, etc..). Some log data will be structured, some will be unstructured (where the schema can change thus).
Storage solution: will need to persist all this data very fast. Will sit on 1-* server(s). It doesn't matter if it's a hybrid solution between filesystem/rdbms/(any suitable flavour of) noSql.
Post processing: the data needs to be queryable ofcourse. E.g. just a key-value store would not suffice, that's a given (maybe for the screenshots only yes).
As a reference, here's a more concrete example:
User runs the client for 2-3 hours (during a "monitoring period"). It sends log data over the wire to the server (storage). Writing speed and data accuracy is vital here.
Management system accumulates the data and makes a report on certain characteristics. All log data should be able to be fetched if needed - but there will be a specific query for a set of users in a given monitoring period. Reading speed is less necessary here, but data accuracy and finding all log parts back eventually is necessary.
If I need to give more information, please let me know.
If you prefer to roll your own rather than use logging packages, I would stick with append only text files. You can certainly encode screenshots in Base64 and keep it in the same file, but I would rather store that separately in the file system with a generated filename stored in the log.
As for reporting, you can obviously read it through a text editor, but if you need a more sophisticated and regular management reporting, you can create an ETL of only the info you report on into a RDBMS. You can always go back and rerun ETL if you decide that you want more info later on.
We have data stored in a data warehouse as follows:
Price
Date
Product Name (varchar(25))
We currently only have four products. That changes very infrequently (on average once every 10 years). Once every business day, four new data points are added representing the day's price for each product.
On the website, a user can request this information by entering a date range and selecting one or more products names. Analytics shows that the feature is not heavily used (about 10 users requests per week).
It was suggested that the data warehouse should daily push (SFTP) a CSV file containing all data (currently 6718 rows of this data and growing by four each day) to the web server. Then, the web server would read data from the file and display that data whenever a user made a request.
Usually, the push would only be once a day, but more than one push could be possible to communicate (infrequent) price corrections. Even in the price correction scenario, all data would be delivered in the file. What are problems with this approach?
Would it be better to have the web server make a request to the data warehouse per user request? Or does this have issues such as a greater chance for network errors or performance issues?
Would it be better to have the web server make a request to the data warehouse per user request?
Yes it would. You have very little data, so there is no need to try and 'cache' this in some way. (Apart from the fact that CSV might not be the best way to do this).
There is nothing stopping you from doing these requests from the webserver to the database server. With as little information as this you will not find performance an issue, but even if it would be when everything grows, there is a lot to be gained on the database-side (indexes etc) that will help you survive the next 100 years in this fashion.
The amount of requests from your users (also extremely small) does not need any special treatment, so again, direct query would be the best.
Or does this have issues such as a greater chance for network errors or performance issues?
Well, it might, but that would not justify your CSV method. Examples and why you need not worry, could be
the connection with the databaseserver is down.
This is an issue for both methods, but with only one connection per day the change of a 1-in-10000 failures might seem to be better for once-a-day methods. But these issues should not come up very often, and if they do, you should be able to handle them. (retry request, give a message to user). This is what enourmous amounts of websites do, so trust me if I say that this will not be an issue. Also, think of what it would mean if your daily update failed? That would present a bigger problem!
Performance issues
as said, this is due to the amount of data and requests, not a problem. And even if it becomes one, this is a problem you should be able to catch at a different level. Use a caching system (non CSV) on the database server. Use a caching system on the webserver. Fix your indexes to stop performance from being a problem.
BUT:
It is far from strange to want your data-warehouse separated from your web system. If this is a requirement, and it surely could be, the best thing you can do is re-create your warehouse-database (the one I just defended as being good enough to query directly) on another machine. You might get good results by doing a master-slave system
your datawarehouse is a master-database: it sends all changes to the slave but is inexcessible otherwise
your 2nd database (on your webserver even) gets all updates from the master, and is read-only. you can only query it for data
your webserver cannot connect to the datawarehouse, but can connect to your slave to read information. Even if there was an injection hack, it doesn't matter, as it is read-only.
Now you don't have a single moment where you update the queried database (the master-slave replication will keep it updated always), but no chance that the queries from the webserver put your warehouse in danger. profit!
I don't really see how SQL injection could be a real concern. I assume you have some calendar type field that the user fills in to get data out. If this is the only form just ensure that the only field that is in it is a date then something like DROP TABLE isn't possible. As for getting access to the database, that is another issue. However, a separate file with just the connection function should do fine in most cases so that a user can't, say open your webpage in an HTML viewer and see your database connection string.
As for the CSV, I would have to say querying a database per user, especially if it's only used ~10 times weekly would be much more efficient than the CSV. I just equate the CSV as overkill because again you only have ~10 users attempting to get some information, to export an updated CSV every day would be too much for such little pay off.
EDIT:
Also if an attack is a big concern, which that really depends on the nature of the business, the data being stored, and the visitors you receive, you could always create a backup as another option. I don't really see a reason for this as your question is currently stated, but it is a possibility that even with the best security an attack could happen. That mainly just depends on if the attackers want the information you have.
I'm considering MongoDB right now. Just so the goal is clear here is what needs to happen:
In my app, Finch (finchformac.com for details) I have thousands and thousands of entries per day for each user of what window they had open, the time they opened it, the time they closed it, and a tag if they choose one for it. I need this data to be backed up online so it can sync to their other Mac computers, etc.. I also need to be able to draw charts online from their data which means some complex queries hitting hundreds of thousands of records.
Right now I have tried using Ruby/Rails/Mongoid in with a JSON parser on the app side sending up data in increments of 10,000 records at a time, the data is processed to other collections with a background mapreduce job. But, this all seems to block and is ultimately too slow. What recommendations does (if anyone) have for how to go about this?
You've got a complex problem, which means you need to break it down into smaller, more easily solvable issues.
Problems (as I see it):
You've got an application which is collecting data. You just need to
store that data somewhere locally until it gets sync'd to the
server.
You've received the data on the server and now you need to shove it
into the database fast enough so that it doesn't slow down.
You've got to report on that data and this sounds hard and complex.
You probably want to write this as some sort of API, for simplicity (and since you've got loads of spare processing cycles on the clients) you'll want these chunks of data processed on the client side into JSON ready to import into the database. Once you've got JSON you don't need Mongoid (you just throw the JSON into the database directly). Also you probably don't need rails since you're just creating a simple API so stick with just Rack or Sinatra (possibly using something like Grape).
Now you need to solve the whole "this all seems to block and is ultimately too slow" issue. We've already removed Mongoid (so no need to convert from JSON -> Ruby Objects -> JSON) and Rails. Before we get onto doing a MapReduce on this data you need to ensure it's getting loaded into the database quickly enough. Chances are you should architect the whole thing so that your MapReduce supports your reporting functionality. For sync'ing of data you shouldn't need to do anything but pass the JSON around. If your data isn't writing into your DB fast enough you should consider Sharding your dataset. This will probably be done using some user-based key but you know your data schema better than I do. You need choose you sharding key so that when multiple users are sync'ing at the same time they will probably be using different servers.
Once you've solved Problems 1 and 2 you need to work on your Reporting. This is probably supported by your MapReduce functions inside Mongo. My first comment on this part, is to make sure you're running at least Mongo 2.0. In that release 10gen sped up MapReduce (my tests indicate that it is substantially faster than 1.8). Other than this you can can achieve further increases by Sharding and directing reads to the the Secondary servers in your Replica set (you are using a Replica set?). If this still isn't working consider structuring your schema to support your reporting functionality. This lets you use more cycles on your clients to do work rather than loading your servers. But this optimisation should be left until after you've proven that conventional approaches won't work.
I hope that wall of text helps somewhat. Good luck!
We're trying to add some kind of persistence in our app.
The app generates about 250 entries per second. Each of these entries belong to one of 2M files. For each file, we want to keep the last 10 entries, so we can look them up later.
The way our client application works :
it gets a stream of all the data
it fetches the right file (GET)
it adds the new content
it saves the file back (PUT)
We're looking for an efficient way to store this data that can scale horizontally as the amount of data we're getting is doubling every few weeks.
We initially looked at S3. It works fine, but becomes very expensive very fast (>$1000 monthly just in PUT operations!)
We then gave a shot at Riak. But it seems we can't get more than 60 write/sec on each node, which is very very slow.
Any other solution out there?
There are lots of knobs you can turn in Riak - ask the mailing list if you haven't already and we'll figure out a sane configuration for you. 60 writes/sec is not within the norm.
See: http://lists.basho.com/mailman/listinfo/riak-users_lists.basho.com
What about Hadoop's HDFS spread over Amazon EC2 instances? I know each instance has a good amount of storage space, and you don't have to pay for put/get, only the inbound transfer.
I would suggest looking at CloudIQ Storage from Appistry. Its a fully distributed file store. Its accessible via a REST-based API, and can run on commodity hardware. You can define the number of copies retained on a file by file basis. It supports an Eventually Consistent model so you can balance file consistency with performance.