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I am getting acquainted with PostgreSQL.
Trying to find information for trigger functions listed below:
RI_Fkey_cascade_del
RI_Fkey_cascade_upd
RI_Fkey_check_ins
RI_Fkey_check_upd
RI_Fkey_noaction_del
RI_Fkey_noaction_upd
RI_Fkey_restrict_del
RI_Fkey_restrict_upd
RI_Fkey_setdefault_del
RI_Fkey_setdefault_upd
RI_Fkey_setnull_del
RI_Fkey_setnull_upd
suppress_redundant_updates_trigger
trigger_in
tsvector_updates_trigger
tsvector_updates_trigger_column
unique_key_recheck
Google shows me only sources of PGSQL.
suppress_redundant_updates_trigger and tsvector_update_trigger are in the documentation.
trigger_in is a placeholder function associated with the TRIGGER pseudotype, and is never actually called.
The rest are implementation details of foreign key and uniqueness constraints. You should find everything you need to know about these constraints in the documentation. You shouldn't need to know anything about the underlying triggers (and if you do, the only explanation you're likely to find is the source code itself).
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Are there companies using this technology in production? I would like to find some benchmarks and use cases. We are considering switching to deepstream with mongo from our current firebase setup. But I want to make sure it can handle and scale well enough to meet our demands.
Additionally, if you are using it, what kinds of tech have you used to load test it?
Maybe you can take a look here where it is stated that:
4 companies reportedly use deepstream.io in their tech stacks, including all, DelightSoft, GraphicWeave, Adinject
Unfortunately, I can not answer your other questions, but you could contact the companies for further info :)
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I have a set of emails which i need to anonymize before I can do any analysis on it. Since sensitive data may be disclosed in the emails, like referring to people, or providing phonenumbers etc.
The tools i have found so far, only uses tables, and mask this, which is quite straight forward to do myself, but it does not solve my problem.
So i am looking for a tool or library, that can help me solve this. I would assume it need to do some Natural language processing in order to analyze how the text is built up, and where refferences to people are being mentioned. I would also appreciate any good articles in this area
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I wanted to know the spell correct api's available for commercial/non commercial usage other than google/bing.
First of all you can write your own spell corrector with this tutorial. In addition there are some Python packages that may help you with that, such as TextBlob (which I highly recommend). Another option is Gingerit which Iv'e never tried but looks promising. Another DIY spell correct tutorial might interest you as well.
https://www.gigablast.com/spellcheckapi.html
I just launched this, so it's still beta, but it's not bad. It has a dictionary of over 600,000,000 entries covering most non-Asian languages.
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http://www.webopedia.com/ is what I'm looking for, but it doesn't offer an API. Are there similar services which offer APIs?
DuckDuckGo has an open API which is quite flexible: take a look at https://duckduckgo.com/api. Using their "define" syntax, it's possible to get back word definitions. For example,
http://api.duckduckgo.com/?q=define+ostensibly&format=json&pretty=1
...would get you back a definition for "ostensibly" in JSON format.
The WordNik API (http://developer.wordnik.com/) is also pretty good for word definitions.
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I'm looking to create a mobile version of the images created on passwordcard.org. All attribution will be given to the site for the data, but I'm looking to implement it where an app doesn't exist yet and network connectivity isn't guaranteed. Is there an externally accessible API available? I didn't really see any information about it in the FAQ.
I'm the creator of passwordcard.org. There is no API, but there is a description of the algorithm used to create the cards so that you can implement it yourself and create the exact same cards as the site would create for the same card numbers. You can find it at http://www.passwordcard.org/algorithm.html