MapReduce from the basics to the actually useful (in under 30 minutes)
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Re: MapReduce from the basics to the actually useful (in under 30 minutes)
#2Terminology is so badly abused in this article that it almost reads like a parody.
Also, the tasks could have been accomplished in 3 or 4 lines of SQL.
Re: MapReduce from the basics to the actually useful (in under 30 minutes)
#3Terminology is so badly abused in this article that it almost reads like a parody. Also, the tasks could have been accomplished in 3 or 4 lines of SQL.
Hi Aheilbut, apologies if I over-simplified. Perhaps I also undersold scalability -- that's a key point of this type of approach, but non-obvious on such a small data example.
Re: MapReduce from the basics to the actually useful (in under 30 minutes)
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Re: MapReduce from the basics to the actually useful (in under 30 minutes)
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Re: MapReduce from the basics to the actually useful (in under 30 minutes)
#6Am I crazy or was there not a single reduce query in the post? I've been working on map reduce queries for a little while now, aggregating statistics over feeds and stories posted, normalizing over item frequencies, and generally doing simple stuff.
I was very excited to see the second half of MapReduce. The reduce queries are always harder to write. If map is addition, reduce is division. I was hoping for examples on different reduce queries that are more advanced than the standard count tags in a list of lists. But no reduce period, so am I missing something?
Re: MapReduce from the basics to the actually useful (in under 30 minutes)
#7Terminology is so badly abused in this article that it almost reads like a parody. Also, the tasks could have been accomplished in 3 or 4 lines of SQL.
In what way is terminology badly abused? There isn't a single use of the word "cloud" in the entire article and the only "buzzword" terminology used appears to be used accurately and sparingly. If that isn't your contention, why not explain otherwise? How does the abuse/misuse of terminology detract from the quality of the article or make it inaccurate?
Further, your argument that the same could be accomplished in "3 or 4 lines of SQL" is a straw man. The article never claimed the specific task was shorter/easier to do using MapReduce and Cloudant; the author made an example based on common use cases—one that didn't require architecting a full requirements specification for when one would find Cloudant/MapReduce/Non-relational databases superior to a few lines of SQL for various values of "superior".
Your comment is vague, largely irrelevant, and completely useless regardless of its accuracy since you provided absolutely no arguments to back up your claims. The fact that it has even two points is disheartening.
Re: MapReduce from the basics to the actually useful (in under 30 minutes)
#8Terminology is so badly abused in this article that it almost reads like a parody. Also, the tasks could have been accomplished in 3 or 4 lines of SQL.
In what way is terminology badly abused? There isn't a single use of the word "cloud" in the entire article and the only "buzzword" terminology used appears to be used accurately and sparingly. If that isn't your contention, why not explain otherwise? How does the abuse/misuse of terminology detract from the quality of the article or make it inaccurate? Further, your argument that the same could be accomplished in "3…
The phrases that irked me were:
"I have yet to meet a database that isn’t a key/value store"
Dimensions that are "somewhat orthogonal"
"It suffices to say that MapReduce is all about giving programmers an efficient way to consume data without needing to know how or where it is actually stored."
I don't think that definition suffices, and it misses (or buries in 'efficient') the rather central point that MapReduce is a programming model for distributing computation.
I'm all for non-relational databases where they're appropriate, and Cloudant sounds like it is doing great things. But I think that there's a risk in presenting toy examples in a way that seems to sell them as the solution to common use cases that really could be solved more easily with old-school tools.
Re: MapReduce from the basics to the actually useful (in under 30 minutes)
#9Terminology is so badly abused in this article that it almost reads like a parody. Also, the tasks could have been accomplished in 3 or 4 lines of SQL.
Hi Aheilbut, apologies if I over-simplified. Perhaps I also undersold scalability -- that's a key point of this type of approach, but non-obvious on such a small data example.
Your people page on your website isn't loading for me in Chrome on OSX, just FYI.
Re: MapReduce from the basics to the actually useful (in under 30 minutes)
#10Am I crazy or was there not a single reduce query in the post? I've been working on map reduce queries for a little while now, aggregating statistics over feeds and stories posted, normalizing over item frequencies, and generally doing simple stuff. I was very excited to see the second half of MapReduce. The reduce queries are always harder to write. If map is addition, reduce is division. I was hoping for examples o…
Plenty of reduce queries, you just didn't need to write the code ;) Good point, though, good material for the next round. I'm open to suggestions if you have an interesting data set or question.