I have an application who is doing a job aggregating data from different Social Network sites Back end processes done Java working great.
Its front end is developed Rails application deadline was 3 weeks for some analytics filter abd report task still few days left almost completed.
When i started implemented map reduce for different states work great over 100,000 record over my local machine work great.
Suddenly my colleague gave me current updated database which 2.7 millions record now my expectation was it would run great as i specify date range and filter before map_reduce execution. My believe was it would result set of that filter but its not a case.
Example
I have a query just show last 24 hour loaded record stats
result comes 0 record found but after 200 seconds with 2.7 million record before it comes in milliseconds..
CODE EXAMPLE BELOW
filter is hash of condition expected to check before map_reduce
map function
reduce function
SocialContent.where(filter).map_reduce(map, reduce).out(inline: true).entries
Suggestion please.. what would be ideal solution in remaining time frame as database is growing exponentially in days.
I would suggest you look at a few different things:
Does all your data still fit in memory? You have a lot more records now, which could mean that MongoDB needs to go to disk a lot more often.
M/R can not make use of indexes. You have not shown your Map and Reduce functions so it's not possible to point out mistakes. Update the question with those functions, and what they are supposed to do and I'll update the answer.
Look at using the Aggregation Framework instead, it can make use of indexes, and also run concurrently. It's also a lot easier to understand and debug. There is information about it at http://docs.mongodb.org/manual/reference/aggregation/
Related
I'm trying to upload about 7 million documents to ES 6.3 and I've been running into and issue where the bulk upload slows to a crawl at about 1 million docs (I have no documents previous to this in the index).
I have a 3 node ES setup with 16GB with 8GB JVM settings, 1 index, 5 shards.
I have turned off refresh ("-1"), set replica to 0, increased the index buffer size to 30%.
On my upload side I have 22 threads running 150 docs per request of bulk insert. This is just a basic ruby script using Postgresql, ActiveRecord, Net/HTTP (For the network call), and and using the ES Bulk API (No gem).
For all of my nodes and upload machines the CPU, Memory, SSD Disk IO is low.
I've been able to get about 30k-40k inserts per/minute, but that seems really slow to me since others have been able to do 2k-3k per/sec. My documents do have nested json, but they don't seem to be very large to me (Is there way to check a single size doc or average?).
I would like to be able to bulk upload these documents in less than 12 - 24hrs and seems like ES should handle that, but once I get to 1 million it seems like it slows to a crawl.
I'm pretty new to ES so any help would be appreciated. I know this seems like question that has already been asked, but I've tried just about everything that I could find and wonder why my upload speed is a factor slower.
I've also checked the logs and only saw some errors about mapping field couldn't change, but nothing about memory over or anything like that.
ES 6.3 is great, but I'm also finding that the API has changed a bunch to 6 and settings that people were using are no longer supported.
I think I found a bottleneck at the active connections to my original database and increased that connection pool which helped, but still slows to a crawl at about 1 Million records, but got to 2 Million over about 8hrs of running.
I also tried an experiment on a big machine, that is used to run the upload job, running 80 threads at 1000 document uploads each. I did some calculations and found out that my documents are about 7-10k per document so doing uploads of 7-10MBs each bulk index. This got to the document count faster to 1M, but once you get there everything slows to a crawl. The machines stats are still really low. I do see output of the threads about every 5 mins or so on the logs for the job, about the same time I see the ES count change.
The ES machines still have low CPU, Memory. The IO is around 3.85MBs and the Network Bandwidth was at 55MBs and drops to about 20MBs.
Any help would be appreciated. Not sure if I should try the ES gem, and use the bulk insert which maybe keeps a connection open, or try something totally different to insert.
ES 6.3 is great, but I'm also finding that the API has changed a bunch to 6 and settings that people were using are no longer supported.
Could you give an example for a breaking change between 6.0 and 6.3 that is a problem for you? We're really trying to avoid those and I can't really recall anything from the top of my head.
I've started profiling that DB and noticed that once you use offset of about 1 Million the queries are starting to take a long time.
Deep pagination is terrible performance wise. There is the great blog post no-offset, which explains
why it's bad: To get the result 1,000 to 1,010 you sort the first 1,010 records, throw away 1,000, and then send 10. The deeper the pagination the more expensive it will be
how to avoid it: Make a unique order of your entries (for example by ID or combine date and ID, but something that is absolute) and add a condition on where to start. For example order by ID, fetch the first 10 entries, and keep the ID of the 10th entry for the next iteration. In that one order by the ID again, but with the condition that the ID must be greater than the last one in your previous run, and fetch the next 10 entries plus remember the last ID again. Repeat until done.
Generally, with your setup you really shouldn't have a problem inserting more than 1 million records. I'd look into the part that is fetching the data first.
I'm using a JVM to perform API calls to the Google Apps Administrator API.
I've noticed with the User Usage Reports, I'm not getting complete data when it comes to a field I'm interested in (num_docs_externally_visible) and the fields which form that fields calculation. I generally request a single day's usage report at a time, across my entire user base (~40k users).
According to the documentation on the developer's, I should be able to see that in a report 2-6 days after; however after running a report for the first 3 weeks of February, I've only gotten it for 60% of the days. The pattern appears to be random (in that I have up to 4 day in a row streaks of the item appearing and 3 days in a row streaks of it not, but there is no consistency to this).
Has anyone else experienced this issue? And if so, were you able to resolve it? Or should I expect this behavior to continue if this is an issue with what the API is returning outside of my control?
I think it's only natural that the data you get is not yet complete, it takes a certain day to receive the complete data.
This SO question is not exactly the same of your question, but i think it will help you. Especially the part that you need to use your account time zone.
Say I have a collection with 100 million records/documents in it.
I want to create a series of reports that involve summing of values in certain columns and grouping by various columns.
What references for XQuery and/or MarkLogic can anyone point me to that will allow me to do this quickly?
I saw cts:avg-aggregate which looks fine. But then I need to group as well..
Also, since I am dealing with a large amount of data and it will take some time to go through it all, I am thinking about setting this up as a job that runs at night to update the report.
I thought of using corb to run through the records and then do something with the output from that. Is this the right approach with MarkLogic and reporting?
Perhaps this guide would help:
http://developer.marklogic.com/blog/group-by-the-marklogic-way
You have several options which are discussed above:
cts:estimate
cts:element-value-co-occurrences
cts:value-tuples + cts:frequency
I have approx. 10 million Article objects in a Mongoid database. The huge number of Article objects makes the queries quite time consuming to perform.
As exemplified below, I am registering for each week (e.g. 700 days from now .. 7 days from now, 0 days from now) how many articles are in the database.
But for every query I make, the time consumption is increased, and Mongoid's CPU usage quickly reaches +100%.
articles = Article.where(published: true).asc(:datetime)
days = Date.today.mjd - articles.first.datetime.to_date.mjd
days.step(0, -7) do |n|
current_date = Date.today - n.days
previous_articles = articles.lt(datetime: current_date)
previous_good_articles = previous_articles.where(good: true).size
previous_bad_articles = previous_articles.where(good: false).size
end
Is there a way to save the Article objects to memory, so only need to call the database on the first line?
A MongoDB database is not build for that.
I think the best way is to run daily a script that creates your data for that day and save it in a Redis Database http://www.redis.io
Redis stores your data in the server memory, so you can access it every time of the day.
And is very quick.
Don't Repeat Yourself (DRY) is a best-practice that applies not only to code but also to processing. Many applications have natural epochs for summarizing data, a day is a good choice in your question, and if the data is historical, it only has to be summarized once. So you reduce processing of 10 million Article document down to 700 day-summary documents. You need special code for merging in today if you want up to the moment accurate data, but the previous savings is well worth the effort.
I politely disagree with the statement, "A MongoDB database is not build for that." You can see from the above that it is all about not repeating processing. The 700 day-summary documents can be stored in any reasonable data store. Since you already are using MongoDB, simply use another MongoDB collection for the day summaries. There's no need to spin up another data store if you don't want to. The summary data will easily fit in memory, and the reduction in processing means that your working set size will no longer be blown out by the historical processing.
Here is the issue.
On a site I've recently taken over it tracks "miles" you ran in a day. So a user can log into the site, add that they ran 5 miles. This is then added to the database.
At the end of the day, around 1am, a service runs which calculates all the miles, all the users ran in the day and outputs a text file to App_Data. That text file is then displayed in flash on the home page.
I think this is kind of ridiculous. I was told they had to do this due to massive performance issues. They won't tell me exactly how they were doing it before or what the major performance issue was.
So what approach would you guys take? The first thing that popped into my mind was a web service which gets the data via an AJAX call. Perhaps every time a new "mile" entry is added, a trigger is fired and updates the "GlobalMiles" table.
I'd appreciate any info or tips on this.
Thanks so much!
Answering this question is a bit difficult since there we don't know all of your requirements and something didn't work before. So here are some different ideas.
First, revisit your assumptions. Generating a static report once a day is a perfectly valid solution if all you need is daily reports. Why hit the database multiple times throghout the day if all that's needed is a snapshot (for instance, lots of blog software used to write html files when a blog was posted rather than serving up the entry from the database each time -- many still do as an optimization). Is the "real-time" feature something you are adding?
I wouldn't jump to AJAX right away. Use the same input method, just move the report from static to dynamic. Doing too much at once is a good way to get yourself buried. When changing existing code I try to find areas that I can change in isolation wih the least amount of impact to the rest of the application. Then once you have the dynamic report then you can add AJAX (and please use progressive enhancement).
As for the dynamic report itself you have a few options.
Of course you can just SELECT SUM(), but it sounds like that would cause the performance problems if each user has a large number of entries.
If your database supports it, I would look at using an indexed view (sometimes called a materialized view). It should support allows fast updates to the real-time sum data:
CREATE VIEW vw_Miles WITH SCHEMABINDING AS
SELECT SUM([Count]) AS TotalMiles,
COUNT_BIG(*) AS [EntryCount],
UserId
FROM Miles
GROUP BY UserID
GO
CREATE UNIQUE CLUSTERED INDEX ix_Miles ON vw_Miles(UserId)
If the overhead of that is too much, #jn29098's solution is a good once. Roll it up using a scheduled task. If there are a lot of entries for each user, you could only add the delta from the last time the task was run.
UPDATE GlobalMiles SET [TotalMiles] = [TotalMiles] +
(SELECT SUM([Count])
FROM Miles
WHERE UserId = #id
AND EntryDate > #lastTaskRun
GROUP BY UserId)
WHERE UserId = #id
If you don't care about storing the individual entries but only the total you can update the count on the fly:
UPDATE Miles SET [Count] = [Count] + #newCount WHERE UserId = #id
You could use this method in conjunction with the SPROC that adds the entry and have both worlds.
Finally, your trigger method would work as well. It's an alternative to the indexed view where you do the update yourself on a table instad of SQL doing it automatically. It's also similar to the previous option where you move the global update out of the sproc and into a trigger.
The last three options make it more difficult to handle the situation when an entry is removed, although if that's not a feature of your application then you may not need to worry about that.
Now that you've got materialized, real-time data in your database now you can dynamically generate your report. Then you can add fancy with AJAX.
If they are truely having performance issues due to to many hits on the database then I suggest that you take all the input and cram it into a message queue (MSMQ). Then you can have a service on the other end that picks up the messages and does a bulk insert of the data. This way you have fewer db hits. Then you can output to the text file on the update too.
I would create a summary table that's rolled up once/hour or nightly which calculates total miles run. For individual requests you could pull from the nightly summary table plus any additional logged miles for the period between the last rollup calculation and when the user views the page to get the total for that user.
How many users are you talking about and how many log records per day?