I would like to create a logger using CouchDB. Basically, everytime someone accesses the file, I would like like to write to the database the username and time the file has been accessed. If this was MySQL, I would just add a row for every access correspond to the user. I am not sure what to do in CouchDB. Would I need to store each access in array? Then what do I do during update, is there a way to append to the document? Would each user have his own document?
I couldn't find any documentation on how to append to an existing document or array without retrieving and updating the entire document. So for every event you log, you'll have to retrieve the entire document, update it and save it to the database. So you'll want to keep the documents small for two reasons:
Log files/documents tend to grow big. You don't want to send large documents across the wire for each new log entry you add.
Log files/documents tend to get updated a lot. If all log entries are stored in a single document and you're trying to write a lot of concurrent log entries, you're likely to run into mismatching document revisions on updates.
Your suggestion of user-based documents sounds like a good solution, as it will keep the documents small. Also, a single user is unlikely to generate concurrent log entries, minimizing any race conditions.
Another option would be to store a new document for each log entry. Then you'll never have to update an existing document, eliminating any race conditions and the need to send large documents between your application and the database.
Niels' answer is going down the right path with transactions. As he said, you will want to create a different document for each access - think of them as actions. Here's what one of those documents might look like
{
"_id": "32 char hash",
"_rev": "32 char hash",
"when": Unix time stamp,
"by": "some unique identifier
}
If you were tracking multiple files, then you'd want to add a "file" field and include a unique identifier.
Now the power of Map/Reduce begins to really shine, as it's extremely good at aggregating multiple pieces of data. Here's how to get the total number of views:
Map:
function(doc)
{
emit(doc.at, 1);
}
Reduce:
function(keys, values, rereduce)
{
return sum(values);
}
The reason I threw the time stamp (doc.at) into the key is that it allows us to get total views for a range of time. Ex., /dbName/_design/designDocName/_view/viewName?startkey=1000&endkey=2000&group=true gives us the total number of views between those two time stamps.
Cheers.
Although Sam's answer is an ok pattern to follow I wanted to point out that there is, indeed, a nice way to append to a Couch document. It just isn't very well documented yet.
By defining an update function in your design document and using that to append to an array inside a couch document you may be able to save considerable disk space. Plus, you end up with a 1:1 correlation between the file you're logging accesses on and the couch doc that represents that file. This is how I imagine a doc might look:
{
"_id": "some/file/path/name.txt",
"_rev": "32 char hash",
"accesses": [
{"at": 1282839291, "by": "ben"},
{"at": 1282839305, "by": "kate"},
{"at": 1282839367, "by": "ozone"}
]
}
One caveat: You will need to encode the "/" as %2F when you request it from CouchDB or you'll get an error. Using slashes in document ids is totally ok.
And here is a pair of map/reduce functions:
function(doc)
{
if (doc.accesses) {
for (i=0; i < doc.accesses.length; i++) {
event = doc.accesses[i];
emit([doc._id, event.by, event.at], 1);
}
}
}
function(keys, values, rereduce)
{
return sum(values);
}
And now we can see another benefit of storing all accesses for a given file in one JSON document: to get a list of all accesses on a document just make a get request for the corresponding document. In this case:
GET http://127.0.0.1:5984/dbname/some%2Ffile%2Fpath%2Fname.txt
If you wanted to count the number of times each file was accessed by each user you'll query the view like so:
GET http://127.0.0.1:5984/test/_design/touch/_view/log?group_level=2
Use group_level=1 if you just want to count total accesses per file.
Finally, here is the update function you can use to append onto that doc.accesses array:
function(doc, req) {
var whom = req.query.by;
var when = Math.round(new Date().getTime() / 1000);
if (!doc.accesses) doc.accesses = [];
var event = {"at": when, "by": whom}
doc.accesses.push(event);
var message = 'Logged ' + event.by + ' accessing ' + doc._id + ' at ' + event.at;
return [doc, message];
}
Now whenever you need to log an access to a file issue a request like the following (depending on how you name your design document and update function):
http://127.0.0.1:5984/my_database/_design/my_designdoc/_update/update_function_name/some%2Ffile%2Fpath%2Fname.txt?by=username
A comment to the last two anwers is that they refer to CouchBase not Apache CouchDb.
It is however possible to define updatehandlers in CouchDb but I have not used it.
http://wiki.apache.org/couchdb/Document_Update_Handlers
Related
I have a set of test results in my mongodb database. Each document in the database contains version information, test data, date, test run information etc...
The version is broken up in the document and stored as individual values. For example: { VER_MAJOR : "0", VER_MINOR : "2", VER_REVISION : "3", VER_PATCH : "20}
My application wants the ability to specify a specific version and grab the document as well as the previous N documents based on the version.
For example:
If version = 0.2.3.20 and n = 5 then the result would return documents with version 0.2.3.20, 0.2.3.19, 0.2.3.18, 0.2.3.17, 0.2.3.16, 0.2.3.15
The solutions that come to my mind is:
Create a new database that contains documents with version information and is sorted. Which can be used to obtain the previous N version's which can be used to obtain the corresponding N documents in the test results database.
Perform the sorting in the test results database itself like in number 1. Though if the test results database is large, this will take a very long time. Also consider inserting in order every time.
Creating another database like in option 1 doesn't seem like the right way. But sorting the test results database seems like there will be lots of overhead, am I mistaken that I should be worried about option 2 producing lots of overhead? I have the impression I'd have to query the entire database then sort it on application side. Querying the entire database seems like overkill...
db.collection_name.find().sort([Paramaters for sorting])
You are quite correct that querying and sorting the entire data set would be very excessive. I probably went overboard on this, but I tried to break everything down in detail below.
Terminology
First thing first, a couple terminology nitpicks. I think you're using the term Database when you mean to use the word Collection. Differentiating between these two concepts will help with navigating documentation and allow for a better understanding of MongoDB.
Collections and Sorting
Second, it is important to understand that documents in a Collection have no inherent ordering. The order in which documents are returned to your app is only applied when retrieving documents from the Collection, such as when specifying .sort() on a query. This means we won't need to copy all of the documents to some other collection; we just need to query the data so that only the desired data is returned in the order we want.
Query
Now to the fun part. The query will look like the following:
db.test_results.find({
"VER_MAJOR" : "0",
"VER_MINOR" : "2",
"VER_REVISION" : "3",
"VER_PATCH" : { "$lte" : 20 }
}).sort({
"VER_PATCH" : -1
}).limit(N)
Our query has a direct match on the three leading version fields to limit results to only those values, i.e. the specific version "0.2.3". A range $lte filter is applied on VER_PATCH since we will want more than a single patch revision.
We then sort results by VER_PATCH to return results descending by the patch version. Finally, the limit operator is used to restrict the number of documents being returned.
Index
We're not done yet! Remember how you said that querying the entire collection and sorting it on the app side felt like overkill? Well, the database would doing exactly that if an index did not exist for this query.
You should follow the equality-sort-match rule when determining the order of fields in an index. In this case, this would give us the index:
{ "VER_MAJOR" : 1, "VER_MINOR" : 1, "VER_REVISION" : 1, "VER_PATCH" : 1 }
Creating this index will allow the query to complete by scanning only the results it would return, while avoiding an in-memory sort. More information can be found here.
I currently have a an application running in the Google App Engine Standard Environment, which, among other things, contains a large database of weather data and a frontend endpoint that generates graph of this data. The database lives in Google Cloud Datastore, and the Python Flask application accesses it via the NDB library.
My issue is as follows: when I try to generate graphs for WeatherData spanning more than about a week (the data is stored for every 5 minutes), my application exceeds GAE's soft private memory limit and crashes. However, stored in each of my WeatherData entities are the relevant fields that I want to graph, in addition to a very large json string containing forecast data that I do not need for this graphing application. So, the part of the WeatherData entities that is causing my application to exceed the soft private memory limit is not even needed in this application.
My question is thus as follows: is there any way to query only certain properties in the entity, such as can be done for specific columns in a SQL-style query? Again, I don't need the entire forecast json string for graphing, only a few other fields stored in the entity. The other approach I tried to run was to only fetch a couple of entities out at a time and split the query into multiple API calls, but it ended up taking so long that the page would time out and I couldn't get it to work properly.
Below is my code for how it is currently implemented and breaking. Any input is much appreciated:
wDataCsv = 'Time,' + ','.join(wData.keys())
qry = WeatherData.time_ordered_query(ndb.Key('Location', loc),start=start_date,end=end_date)
for acct in qry.fetch():
d = [acct.time.strftime(date_string)]
for attr in wData.keys():
d.append(str(acct.dict_access(attr)))
wData[attr].append([acct.time.strftime(date_string),acct.dict_access(attr)])
wDataCsv += '\\n' + ','.join(d)
# Children Entity - log of a weather at parent location
class WeatherData(ndb.Model):
# model for data to save
...
# Function for querying data below a given ancestor between two optional
# times
#classmethod
def time_ordered_query(cls, ancestor_key, start=None, end=None):
return cls.query(cls.time>=start, cls.time<=end,ancestor=ancestor_key).order(-cls.time)
EDIT: I tried the iterative page fetching strategy described in the link from the answer below. My code was updated to the following:
wDataCsv = 'Time,' + ','.join(wData.keys())
qry = WeatherData.time_ordered_query(ndb.Key('Location', loc),start=start_date,end=end_date)
cursor = None
while True:
gc.collect()
fetched, next_cursor, more = qry.fetch_page(FETCHNUM, start_cursor=cursor)
if fetched:
for acct in fetched:
d = [acct.time.strftime(date_string)]
for attr in wData.keys():
d.append(str(acct.dict_access(attr)))
wData[attr].append([acct.time.strftime(date_string),acct.dict_access(attr)])
wDataCsv += '\\n' + ','.join(d)
if more and next_cursor:
cursor = next_cursor
else:
break
where FETCHNUM=500. In this case, I am still exceeding the soft private memory limit for queries of the same length as before, and the query takes much, much longer to run. I suspect the problem may be with Python's garbage collector not deleting the already used information that is re-referenced, but even when I include gc.collect() I see no improvement there.
EDIT:
Following the advice below, I fixed the problem using Projection Queries. Rather than have a separate projection for each custom query, I simply ran the same projection each time: namely querying all properties of the entity excluding the JSON string. While this is not ideal as it still pulls gratuitous information from the database each time, generating individual queries of each specific query is not scalable due to the exponential growth of necessary indices. For this application, as each additional property is negligible additional memory (aside form that json string), it works!
You can use projection queries to fetch only the properties of interest from each entity. Watch out for the limitations, though. And this still can't scale indefinitely.
You can split your queries across multiple requests (more scalable), but use bigger chunks, not just a couple (you can fetch 500 at a time) and cursors. Check out examples in How to delete all the entries from google datastore?
You can bump your instance class to one with more memory (if not done already).
You can prepare intermediate results (also in the datastore) from the big entities ahead of time and use these intermediate pre-computed values in the final stage.
Finally you could try to create and store just portions of the graphs and just stitch them together in the end (only if it comes down to that, I'm not sure how exactly it would be done, I imagine it wouldn't be trivial).
I am new to cloudant , no-sql data base (i had worked on mongodb )
1) is there any cloudant ui to write the queires to find the resultset for developing.
2) how to create map-reduce in cloudant ?..
can u please reply me or send your thoughts.
The search indexes are written in JavaScript (at the moment, Cloduant has launched their own "Cloudant Query" which promises to be easier to work with but I haven't had the time to try it properly yet.)
Say you have documents in your DB which contain a field called "UserName" and you want to create a view on all these. You could write a function like this;
function(doc) {
if ( typeof doc.UserName !== "undefined" ) {
emit([doc.UserName], doc._id);
}
}
For example (it will output the user names and document ids)
If a given user name could be associated with multiple documents you could do this, for example;
function(doc) {
if ( typeof doc.UserName !== "undefined" ) {
emit([doc.UserName,doc._id], 1);
}
}
and also use the built-in "count" or "sum" reduce functions that Cloudant provides to tally the number of documents a given user name is associated with etc.
You can use the UI in the Cloudant DB dashboard to execute queries or (as I personally favour) use a tool like Postman (https://www.getpostman.com/)
One word of warning though; error- and sanity -checking of your JavaScript code is pretty much non-existent and you'll only know that something isn't working when you hit "save & build index" which can be a major pain if you're working on large databases (it can grind the whole thing to a halt). A pro tip, therefore, is to work out your indexes on smaller data sets in some safe little sandbox database before you let it lose on anything important...
All of this is supposedly going to be Much Better with Cloudant Query.
Yet another potentially embarrassing question. Please feel free to point any obvious solution that may have been overlooked - I have searched for solutions previously and found nothing, but sometimes it's a matter of choosing the wrong keywords to search for.
Here's the situation: coded my own RequestHandler a few months ago for an enterprise-y system, in order to inject a few necessary security parameters as an extra filter in all queries made to the solr core. Everything runs smoothly until the part where the docs resulting from a query to the index are collected and then returned to the user.
Basically after the filter is created and the query is executed we get a set of document ids (and scores), but then we have to iterate through the ids in order to build the result set, one hit at a time - which is a good 10x slower that querying the standard requesthandler, and only bound to get worse as the number of results increase. Even worse, since our schema heavily relies on dynamic fields for flexibility, there is no way (that I know of) of previously retrieving the list of fields to retrieve per document, other than testing all possible combinations per doc.
The code below is a simplified version of the one running in production, for querying the SolrIndexSearcher and building the response.
Without further ado, my questions are:
is there any way of retrieving all results at once, instead of building a response document by document?
is there any possibility of getting the list of fields on each result, instead of testing all possible combinations?
any particular WTFs in this code that I should be aware of? Feel free to kick me!
//function that queries index and handles results
private void searchCore(SolrIndexSearcher searcher, Query query,
Filter filter, int num, SolrDocumentList results) {
//Executes the query
TopDocs col = searcher.search(query,filter, num);
//results
ScoreDoc[] docs = col.scoreDocs;
//iterate & build documents
for (ScoreDoc hit : docs) {
Document doc = reader.document(hit.doc);
SolrDocument sdoc = new SolrDocument();
for(Object f : doc.getFields()) {
Field fd = ((Field) f);
//strings
if (fd.isStored() && (fd.stringValue() != null))
sdoc.addField(fd.name(), fd.stringValue());
else if(fd.isStored()) {
//Dynamic Longs
if (fd.name().matches(".*_l") ) {
ByteBuffer a = ByteBuffer.wrap(fd.getBinaryValue(),
fd.getBinaryOffset(), fd.getBinaryLength());
long testLong = a.getLong(0);
sdoc.addField(fd.name(), testLong );
}
//Dynamic Dates
else if(fd.name().matches(".*_dt")) {
ByteBuffer a = ByteBuffer.wrap(fd.getBinaryValue(),
fd.getBinaryOffset(), fd.getBinaryLength());
Date dt = new Date(a.getLong());
sdoc.addField(fd.name(), dt );
}
//...
}
}
results.add(sdoc);
}
}
Per OPs request:
Although this doesn't answer your specific question, I would suggest another option to solve your problem.
To add a Filter to all queries, you can add an "appends" section to the StandardRequestHandler in the SolrConfig.xml file. Add a "fl" (stands for filter) section and add your filter. Every request piped through the StandardRequestHandler will have the filter appended to it automatically.
This filter is treated like any other, so it is cached in the FilterCache. The result is fairly fast filtering (through docIds) at query time. This may allow you to avoid having to pull the individual documents in your solution to apply the filtering criteria.
When I first started developing this project, there was no requirement for generating large files, however it is now a deliverable.
Long story short, GAE just doesn't play nice with any large scale data manipulation or content generation. The lack of file storage aside, even something as simple as generating a pdf with ReportLab with 1500 records seems to hit a DeadlineExceededError. This is just a simple pdf comprised of a table.
I am using the following code:
self.response.headers['Content-Type'] = 'application/pdf'
self.response.headers['Content-Disposition'] = 'attachment; filename=output.pdf'
doc = SimpleDocTemplate(self.response.out, pagesize=landscape(letter))
elements = []
dataset = Voter.all().order('addr_str')
data = [['#', 'STREET', 'UNIT', 'PROFILE', 'PHONE', 'NAME', 'REPLY', 'YS', 'VOL', 'NOTES', 'MAIN ISSUE']]
i = 0
r = 1
s = 100
while ( i < 1500 ):
voters = dataset.fetch(s, offset=i)
for voter in voters:
data.append([voter.addr_num, voter.addr_str, voter.addr_unit_num, '', voter.phone, voter.firstname+' '+voter.middlename+' '+voter.lastname ])
r = r + 1
i = i + s
t=Table(data, '', r*[0.4*inch], repeatRows=1 )
t.setStyle(TableStyle([('ALIGN',(0,0),(-1,-1),'CENTER'),
('INNERGRID', (0,0), (-1,-1), 0.15, colors.black),
('BOX', (0,0), (-1,-1), .15, colors.black),
('FONTSIZE', (0,0), (-1,-1), 8)
]))
elements.append(t)
doc.build(elements)
Nothing particularly fancy, but it chokes. Is there a better way to do this? If I could write to some kind of file system and generate the file in bits, and then rejoin them that might work, but I think the system precludes this.
I need to do the same thing for a CSV file, however the limit is obviously a bit higher since it's just raw output.
self.response.headers['Content-Type'] = 'application/csv'
self.response.headers['Content-Disposition'] = 'attachment; filename=output.csv'
dataset = Voter.all().order('addr_str')
writer = csv.writer(self.response.out,dialect='excel')
writer.writerow(['#', 'STREET', 'UNIT', 'PROFILE', 'PHONE', 'NAME', 'REPLY', 'YS', 'VOL', 'NOTES', 'MAIN ISSUE'])
i = 0
s = 100
while ( i < 2000 ):
last_cursor = memcache.get('db_cursor')
if last_cursor:
dataset.with_cursor(last_cursor)
voters = dataset.fetch(s)
for voter in voters:
writer.writerow([voter.addr_num, voter.addr_str, voter.addr_unit_num, '', voter.phone, voter.firstname+' '+voter.middlename+' '+voter.lastname])
memcache.set('db_cursor', dataset.cursor())
i = i + s
memcache.delete('db_cursor')
Any suggestions would be very much appreciated.
Edit:
Above I had documented three possible solutions based on my research, plus suggestions etc
They aren't necessarily mutually exclusive, and could be a slight variation or combination of any of the three, however the gist of the solutions are there. Let me know which one you think makes the most sense, and might perform the best.
Solution A: Using mapreduce (or tasks), serialize each record, and create a memcache entry for each individual record keyed with the keyname. Then process these items individually into the pdf/xls file. (use get_multi and set_multi)
Solution B: Using tasks, serialize groups of records, and load them into the db as a blob. Then trigger a task once all records are processed that will load each blob, deserialize them and then load the data into the final file.
Solution C: Using mapreduce, retrieve the keynames and store them as a list, or serialized blob. Then load the records by key, which would be faster than the current loading method. If I were to do this, which would be better, storing them as a list (and what would the limitations be...I presume a list of 100,000 would be beyond the capabilities of the datastore) or as a serialized blob (or small chunks which I then concatenate or process)
Thanks in advance for any advice.
Here is one quick thought, assuming it is crapping out fetching from the datastore. You could use tasks and cursors to fetch the data in smaller chunks, then do the generation at the end.
Start a task which does the initial query and fetches 300 (arbitrary number) records, then enqueues a named(!important) task that you pass the cursor to. That one in turn queries [your arbitrary number] records, and then passes the cursor to a new named task as well. Continue that until you have enough records.
Within each task process the entities, then store the serialized result in a text or blob property on a 'processing' model. I would make the model's key_name the same as the task that created it. Keep in mind the serialized data will need to be under the API call size limit.
To serialize your table pretty fast you could use:
serialized_data = "\x1e".join("\x1f".join(voter) for voter in data)
Have the last task (when you get enough records) kick of the PDf or CSV generation. If you use key_names for you models you, should be able to grab all of the entities with encoded data by key. Fetches by key are pretty fast, you'll know the model's keys since you know the last task name. Again, you'll want to be mindful size of your fetches from the datastore!
To deserialize:
list(voter.split('\x1f') for voter in serialized_data.split('\x1e'))
Now run your PDF / CSV generation on the data. If splitting up the datastore fetches alone does not help you'll have to look into doing more of the processing in each task.
Don't forget in the 'build' task you'll want to raise an exception if any of the interim models are not yet present. Your final task will automatically retry.
Some time ago I faced the same problem with GAE. After many attempts I just moved to another web hosting since I could do it. Nevertheless, before moving I had 2 ideas how to resolve it. I haven't implemented them, but you may try to.
First idea is to use SOA/RESTful service on another server, if it is possible. You can even create another application on GAE in Java, do all the work there (I guess with Java's PDFBox it will take much less time to generate PDF), and return result to Python. But this option needs you to know Java and also to divide your app to several parts with terrible modularity.
So, there's another approach: you can create a "ping-pong" game with a user's browser. The idea is that if you cannot make everything in a single request, force browser to send you several. During first request make only a part of work which fits 30 seconds limit, then save the state and generate 'ticket' - unique identifier of a 'job'. Finally, send the user response which is simple page with redirect back to your app, parametrized by a job ticket. When you get it. just restore state and proceed with the next part of job.