As it says on the tin. I know that it will be somewhere between 125 and 2013 but trying to streamline my code.
Any help greatly appreciated!
The DOCS say 1000 is the maximum (see the Limitations section). You could reach the limits of displaying tables in the output in memory, making the effective number smaller (depends on the style of tables you have it output as well as the number of rows displayed in the tables).
This isn't to say that outputting 1,000 frequencies is ever a really good idea. Why would you ever want to visually pour over that many tables?
Related
We recently started to encounter this error:
{"error":"partial write: max-series-per-database limit exceeded: (1000000) dropped=1"}
When writing metric data like this:
resque_job,environment=beta,billing_status=active-current,billing_active=active,instance_id=1103,instance_testmode=0,instance_staging=0,server_addr=RESQUE,database_host=db11.msp1.our-domain.com,admin_sso_key=_EMPTY_,admin_is_internal=_EMPTY_,queue_priority=default seconds_spent_job=0.20966601371765,number_in_batch=1 1649203450783000002
I know that Influx recommends you keep your series cardinality low, and our impression was that series cardinality would mean keeping each tag individually to a small number of values. e.g. we felt comfortable sending instance_id=1103 as a tag, because we know that there will never be more than 2000 distinct instance_id tag values.
But after running into this error... I'm afraid maybe I was mistaken here. Do we actually need to keep the cardinality of all possible combinations of all tags low? e.g. do these two things count as two separate series towards the 1,000,000 default max, because the instance_id is different?
resque_job,environment=beta,billing_status=active-current,billing_active=active,instance_id=1111,instance_testmode=0,instance_staging=0,server_addr=RESQUE,database_host=db11.msp1.our-domain.com,admin_sso_key=_EMPTY_,admin_is_internal=_EMPTY_,queue_priority=default seconds_spent_job=0.20966601371765,number_in_batch=1 1649203450783000002
resque_job,environment=beta,billing_status=active-current,billing_active=active,instance_id=2222,instance_testmode=0,instance_staging=0,server_addr=RESQUE,database_host=db11.msp1.our-domain.com,admin_sso_key=_EMPTY_,admin_is_internal=_EMPTY_,queue_priority=default seconds_spent_job=0.20966601371765,number_in_batch=1 1649203450783000002
If those count as two separate series... then is there a better way to structure this data in Influx? 1,000,000 total seems like a tiny amount if each separate combination of tags is a separate series...
Does InfluxDB 2.x help with this?
Is there a better tool that can handle a large number of tags and not bump into limits like this?
There is no way to figure out what data was not recorded. Update the max-series-per-database configuration to be more than 1M in order to stop dropping data.
This can be an indication that you are creating a lot of series. i saw some documentation on why that isn't great.
Hope this helps!
I have 93 arrays. Each array has about 18 values in average
I need to make a product of these arrays.
So I have my two dimension array that store these 93 arrays.
Here is what I try to do
DATASET.first.product(*DATASET[1..-1])
Ruby returns
RangeError: too big to product
Does anyone know some workaround to figure out of it?
Some ways to chunk them?
What you want is impossible.
The product of 93 arrays with ~18 elements each is an array with approximately 549975033204266172374216967425209467080301768557741749051999338598022831065169332830885722071173603516904554174087168 elements, each of which is a 93-element array.
This means you need 549975033204266172374216967425209467080301768557741749051999338598022831065169332830885722071173603516904554174087168 * 93 * 64bit of memory to store it, which is roughly 409181424703974032246417423764355843507744515806959861294687507916928986312485983626178977220953161016576988305520852992 bytes. That is about 40 orders of magnitude more than the number of particles in the universe. In other words, even if you were to convert the entire universe into RAM, you would still need to find a way to store on the order of 827180612553027 yobibyte on each and every particle in the universe; that is about 6000000000000000000000000 times the information content of the World Wide Web and 10000000000000000000000 times the information content of the dark web.
Does anyone know some workaround to figure out of it? Some ways to chunk them?
Even if you process them in chunks, that doesn't change the fact that you still need to process 51147678087996754030802177970544480438468064475869982661835938489616123289060747953272372152619145127072123538190106624 elements. Even if you were able to process one element per CPU instruction (which is unrealistic, you will probably need dozens if not hundreds of instructions), and even if each instruction only takes one clock cycle (which is unrealistic, on current mainstream CPUs, each instruction takes multiple clock cycles), and even if you had a terahertz CPU (which is unrealistic, the fastest current CPUs top out at 5 GHz), and even if your CPU had a million cores (which is unrealistic, even GPUs only have a couple of thousand extremely simple cores), and even if your motherboard had a million sockets (which is unrealistic, mainstream motherboards only have a maximum of 4 sockets, and even the biggest supercomputers only have 10 million cores in total), and even if you had a million of those computers in a cluster, and even if you had a million of those clusters in a supercluster, and even if you had a million friends that also have a supercluster like this, it would still take you about 1621000000000000000000000000000000000000000000000000000000000000000000 years to iterate through them.
Right, so as it is hopefully clear that this should not be attempted I'll take a risk and attempt solving your actual problem.
You've mentioned in the comments that you need this array for property testing - I'll take a massive leap of faith here and assume you want to test that every possible combination satisfies some conditions - and this is the mistake here, as the amount of possible combination is just... large...
Instead, you can test that some of the combinations works. You can easily generate a short, randomized list of combinations using:
Array.new(num) { DATASET.map(&:sample) }
Where num is a number of combinations you want to test. Note that there is a chance that some of the entries will be duplicated - but given your dataset size the chances would be comparable with colliding uuids and can be safely ignored.
Generating such a subset of possible solutions is much easier, faster and, most importantly, possible. Since the output is randomized, it will test slightly different combination on each run, so remember to have some randomization setup in your test suite if you want to be able to recreate failures.
I have some questions in my mind related to wrk2 benchmark tool. I did a lot of search on them and did not find answers related to them. If you have little understanding related to them then please help me.
What "count" column represents in Detailed Percentile spectrum? example Did they show the total number of requests whose latency is within "value" (column name) range? Correct me if i am wrong.
What "latency(i)" and "requests" represent in done function provided by wr2 and wrk? and How can I get that values? done_function
How can I get the total number of requests generated per minute and their latencies? Does "latency(i)" and "requests" give me some information about them?
What "-B (batch latency)" option in wrk does? My output remains the same whether i use this option or not. batch
In wrk2 readme.md, i didn't understand these lines. can you please explain that.
I have a very small question which has been baffling me for a while. I have a dataset with interesting features, but some of them are dimensionless quantities (I've tried using z-scores) on them but they've made things worse. These are:
Timestamps (Like YYYYMMDDHHMMSSMis) I am getting the last 9 chars from this.
User IDs (Like in a Hash form) How do I extract meaning from them?
IP Addresses (You know what those are). I only extract the first 3 chars.
City (Has an ID like 1,15,72) How do I extract meaning from this?
Region (Same as city) Should I extract meaning from this or just leave it?
The rest of the things are prices, widths and heights which understand. Any help or insight would be much appreciated. Thank you.
Timestamps can be transformed into Unix Timestamps, which are reasonable natural numbers
User IF/Cities/Regions are nominal values, which has to be encoded somehow. The most common approach is to create as much "dummy" dimensions as the number of possible values. So if you have 100 ciries, than you create 100 dimensions and give "1" only on the one representing a particular city (and 0 on the others)
IPs should rather be removed, or transformed into some small group of them (based on the DNS-network identification and nominal to dummy transformation as above)
My platform here is Ruby - a webapp using Rails 3.2 in particular.
I'm trying to match objects (people) based on their ratings for certain items. People may rate all, some, or none of the same items as other people. Ratings are integers between 0 and 5. The number of items available to rate, and the number of users, can both be considered to be non-trivial.
A quick illustration -
The brute-force approach is to iterate through all people, calculating differences for each item. In Ruby-flavoured pseudo-code -
MATCHES = {}
for each (PERSON in (people except USER)) do
for each (RATING that PERSON has made) do
if (USER has rated the item that RATING refers to) do
MATCHES[PERSON's id] += difference between PERSON's rating and USER's rating
end
end
end
lowest values in MATCHES are the best matches for USER
The problem here being that as the number of items, ratings, and people increase, this code will take a very significant time to run, and ignoring caching for now, this is code that has to run a lot, since this matching is the primary function of my app.
I'm open to cleverer algorithms and cleverer databases to achieve this, but doing it algorithmically and as such allowing me to keep everything in MySQL or PostgreSQL would make my life a lot easier. The only thing I'd say is that the data does need to persist.
If any more detail would help, please feel free to ask. Any assistance greatly appreciated!
Check out the KD-Tree. It's specifically designed to speed up neighbour-finding in N-Dimensional spaces, like your rating system (Person 1 is 3 units along the X axis, 4 units along the Y axis, and so on).
You'll likely have to do this in an actual programming language. There are spatial indexes for some DBs, but they're usually designed for geographic work, like PostGIS (which uses GiST indexing), and only support two or three dimensions.
That said, I did find this tantalizing blog post on PostGIS. I was then unable to find any other references to this, but maybe your luck will be better than mine...
Hope that helps!
Technically your task is matching long strings made out of characters of a 5 letter alphabet. This kind of stuff is researched extensively in the area of computational biology. (Typically with 4 letter alphabets). If you do not know the book http://www.amazon.com/Algorithms-Strings-Trees-Sequences-Computational/dp/0521585198 then you might want to get hold of a copy. IMHO this is THE standard book on fuzzy matching / scoring of sequences.
Is your data sparse? With rating, most of the time not every user rates every object.
Naively comparing each object to every other is O(n*n*d), where d is the number of operations. However, a key trick of all the Hadoop solutions is to transpose the matrix, and work only on the non-zero values in the columns. Assuming that your sparsity is s=0.01, this reduces the runtime to O(d*n*s*n*s), i.e. by a factor of s*s. So if your sparsity is 1 out of 100, your computation will be theoretically 10000 times faster.
Note that the resulting data will still be a O(n*n) distance matrix, so strictl speaking the problem is still quadratic.
The way to beat the quadratic factor is to use index structures. The k-d-tree has already been mentioned, but I'm not aware of a version for categorical / discrete data and missing values. Indexing such data is not very well researched AFAICT.