137 karma · joined January 9, 2011
Many people go to supermarkets; they’re like central hubs in a network. It doesn’t mean that as much transmission happen there as in smaller confined places like houses and bars.
You will be able to do efficient range queries on any of these. Most use-cases will work best when you partition your data into time chunks, which commonly would be the time you inserted the samples into the database (created time in your case I guess).
- Significant TSM encoding and decoding performance improvements.
- The TSI index will be on by default.
- Queries that use the same tag key/value filters will be answered from the index more quickly using an LRU cache.
- Field keys will now be indexed in 2.0, making filtering/grouping on field keys more efficient.
- Improvements to how series are extracted from the index, and points data from the TSM engine, which helps with memory performance for queries.
- Significant performance improvements to measurement deletion.
> And can data points be incremented instead of the current field-replacement crap when you get new points with the same tag set?Can you elaborate on that?
If you look at a tax calculator you will see that for a Swedish salary equivalent to $60K, the effective tax rate is actually ~27%.
$60K USD == ~540K SEK, which is 45K SEK a month.
Plugging 45,000 into [1] and choosing the municipality of Stockholm results in a net monthly income of 32,587 SEK.
That's a tax rate of ~27%...
We're looking for database engineers to help work on InfluxDB and other products. The current InfluxDB team is spread across four timezones and two continents; we're definitely a remote-first company.
If you love Go, open-source, writing high performance code and solving interesting data/distributed systems problems, then we want to hear from you.
We're hiring for a number of roles: https://www.influxdata.com/careers/
No puzzles and technical interviews for us; if you're an exceptional Go candidate you'll probably stand out already with your previous work or software projects.
~ $ go get -u github.com/otoolep/syslog-gollector
warning: code.google.com is shutting down; import path code.google.com/p/log4go will stop working
warning: code.google.com is shutting down; import path code.google.com/p/snappy-go/snappy will stop working
~ $
Everything is fine (aside from the warnings about code.google shutting down).Yes, Go does not have a culture of versioning, but it _does_ have a culture of _vendoring_. You should be getting your reproducible builds by vendoring your dependencies. That way, you don't need to do any hunting or git-bisect shenanigans.
You can describe GET requests as follows with cURL, which I find a bit smarter :-)
curl -G https://www.shirts.io/api/v1/quote/ \
-d "api_key=APIKEY" \
-d "garment[0][product_id]=3" \
-d "garment[0][color]=White" \
-d "garment[0][sizes][med]=100" \
-d "garment[0][sizes][lrg]=50"
In cases where you need to url-encode the parameter, you can switch out -d for --data-urlencodeI would have thought getting to the root cause of your “HN addiction” would probably be a better alternative.
I don't know anyone that doesn't use a third-party client. And, given Twitter's clear desire to restrict 3rd party client API access, in order to get more eyeballs on more easily monetizable web-pages, Twitter Cards are never going to be seen by many users.
For example, if the dozens of papers are all disjointed, or on completely different topics, you can simply bolt them all together and call it a PhD thesis.
Like all good stories, there has to be a solid start, middle and end. This generally also means that one fantastic paper is also not enough, content wise, to warrant the award of a PhD.
You pay for your tuition by having x% of your salary deducted from future pay packets.
This does not “fix” anything. All it does is make the cost of education higher for those who earn less, like teachers and nurses. Why? Because they take longer to pay off their loans than Engineers, Lawyers, and those in Finance.
An let’s not even get on to the point that Universities are really there for research as well as teaching. We shouldn't be incentivising them to solely churn out future Software Engineers, etc...
An n-armed bandit problem is a type of reinforcement learning problem, and the associated algorithms for solving these problems are considered machine learning.
Also, in terms of applying ML, you could scoot through Andrew Ng's Machine Learning Coursera course (not sure if it's running at the moment, though).
Finally... One tip – typically I have always found that when you want to start apply ML to real-world problemss, start simple and only iterate when the results of your approach are not satisficing. This is usually because all the bleeding edge ML research/techniques don't consider a shit-load of real-world issues, like scaleability, applicability to wide-range of problem, unstructured or noisy data and so on.
Just say it like it is, without putting a slant on it - A's response rate was 0.6% higher than B's.
I don't care if the results are significantly different, if the difference between the two samples is so small.
That’s amused me somewhat chuckles