Earlier quoted context omitted.
Can you elaborate on what it is that slows down your app performance? Is it bottle-necking on writing to the database? Does the database have read activity? Does the application slow down when there is read activity? With some basic assumptions on my part including that you can have a delay in writing data to the database (since it's archival and analysis), but you don't want the application to be delayed, putting a…
Yes, we only have one DB for everything and it is bottle-necking on writes. Queuing the writes is probably the easiest solution since we already have RabbitMQ setup... thanks for your answer.
Beringei: A high-performance time series storage engine
41–50 of 58 posts
Re: Beringei: A high-performance time series storage engine
#42Earlier quoted context omitted.
I'm not sure I follow everything what you wrote. I'm interested in how many inserts are you doing per second to the DB. Is it in 1000s/sec or millions/sec.
Millions, not thousands, of inserts per second. TrailDB is hella fast (that's why we use it).
Re: Beringei: A high-performance time series storage engine
#43Earlier quoted context omitted.
I'm not sure I follow everything what you wrote. I'm interested in how many inserts are you doing per second to the DB. Is it in 1000s/sec or millions/sec.
Millions, not thousands, of inserts per second. TrailDB is hella fast (that's why we use it).
Re: Beringei: A high-performance time series storage engine
#44Earlier quoted context omitted.
CSV Same way most web servers log traffic.
Or perhaps SQLite, since it should perform similar to writing to a CSV file and you get querying out of the box afterwards.
Re: Beringei: A high-performance time series storage engine
#45Context: initial commit with 277,989 additions [0].
[0] - https://github.com/facebookincubator/beringei/commit/17a6c2d...
Re: Beringei: A high-performance time series storage engine
#46We're getting good results from http://traildb.io/ and we don't have to grant Facebook a worldwide, royalty-free license to our patent pool in order to use it.
We would love to hear how you are using TrailDB and what could be improved for your use case. Feel free to open issues in GitHub or drop by at our Gitter channel, https://gitter.im/traildb/traildb
Re: Beringei: A high-performance time series storage engine
#47Earlier quoted context omitted.
I don't think TrailDB is comparable to Beringei. TrailDB is built for behavioral analytics queries while Beringei is developed for time-series aggregated metrics.
> time-series aggregated metrics That's exactly what we're using TrailDB for. Works great.
Re: Beringei: A high-performance time series storage engine
#48Earlier quoted context omitted.
I'm curious about what kind of write speeds you are using traildb for?
We trace every server request with it, across all stages of the request—approx. 10-12 events/timestamps per request—across multiple processes (think Zipkin). The per-request event streams are independent "trails" per process, and then we merge them together later and compute aggregated metrics using hdr_histogram. Individual events are typically hundreds to thousands of nanoseconds long, with about 20 nanoseconds of…
Re: Beringei: A high-performance time series storage engine
#49Earlier quoted context omitted.
I'm not sure I follow everything what you wrote. I'm interested in how many inserts are you doing per second to the DB. Is it in 1000s/sec or millions/sec.
Millions, not thousands, of inserts per second. TrailDB is hella fast (that's why we use it).
Re: Beringei: A high-performance time series storage engine
#50What would HN suggest to store about 1GB of data per day, mostly for archiving and offline analysis, with less than 10 columns including timestamp? We're currently writing everything to our postgres DB and flushing the table to S3 every few days but it's killing the app performance under high loads. I'm looking for something that is easy to set up and keep running with low to no maintenance.
Given the "archive and offline analysis" requirements, which are special characteristics. The "hacked" way is to go CSV files, store them on s3. It is very limited but it is also very simple. (Not sure what software can run queries on CSV files directly). The "doing things right" way is to go for AWS RedShift or Google BigQuery. There is a learning curve at the start but it's really REALLY good and it will pay off by…