I have a large database (~100Gb) from which I need to pull every entry,
perform some comparisons on it, and then store the results of those comparisons. I have attempted to run parallel queries within a single R sessions without any success. I can just run multiple R sessions all at once but I am looking for a better approach. Here is what I attempted:
library(RSQLite)
library(data.table)
library(foreach)
library(doMC)
#---------
# SETUP
#---------
#connect to db
db <- dbConnect(SQLite(), dbname="genes_drug_combos.sqlite")
#---------
# QUERY
#---------
# 856086 combos = 1309 * 109 * 6
registerDoMC(8)
#I would run 6 seperate R sessions (one for each i)
res_list <- foreach(i=1:6) %dopar% {
a <- i*109-108
b <- i*109
pb <- txtProgressBar(min=a, max=b, style=3)
res <- list()
for (j in a:b) {
#get preds for drug combos
statement <- paste("SELECT * from combo_tstats WHERE rowid BETWEEN", (j*1309)-1308, "AND", j*1309)
combo_preds <- dbGetQuery(db, statement)
#here I do some stuff to the result returned from the query
combo_names <- combo_preds$drug_combo
combo_preds <- as.data.frame(t(combo_preds[,-1]))
colnames(combo_preds) <- combo_names
#get top drug combos
top_combos <- get_top_drugs(query_genes, drug_info=combo_preds, es=T)
#update progress and store result
setTxtProgressBar(pb, j)
res[[ length(res)+1 ]] <- top_combos
}
#bind results together
res <- rbindlist(res)
}
I dont get any errors but only one core spins up. In contrast, if I run multiple R sessions, all my cores go at it. What am I doing wrong?
Some things I have learned while accessing concurrently with RSQLite the same file SQLite database:
1. Make sure each worker has its own DB connection.
parallel::clusterEvalQ(cl = cl, {
db.conn <- RSQLite::dbConnect(RSQLite::SQLite(), "./export/models.sqlite");
RSQLite::dbClearResult(RSQLite::dbSendQuery(db.conn, "PRAGMA busy_timeout=5000;"));
})
2. Use PRAGMA busy_timeout=5000;
By default this is set to 0, and chances are that you will end up with a "database is locked" error each time your worker tries to write to the DB while it is locked. Previous code sets this PRAGMA in each worker connection. Note that SELECT operations are never locked, only INSERT/DELETE/UPDATE.
3. Use PRAGMA journal_mode=WAL;
This only has to be set once and stays on by default forever. It will add two (more or less permanent) files to the DB. It will improve concurrent read/write performance. Read more here.
With the above settings I have not experienced this issue.
Related
Use Case
I have some terabytes of US property data to merge. It is spread across two distinct file formats and thousands of files. The source data is split geographically.
I can't find a way to branch a single pipeline into many independent processing flows.
This is especially difficult because the Dataframe API doesn't seem to support a PTransform on a collection of filenames.
Detailed Background
The distribution of files is like this:
StateData - 51 total files (US states + DC)
CountyData - ~2000 total files (county specific, grouped by state)
The ideal pipeline would split into thousands of independent processing steps and complete in minutes.
1 -> 51 (each US state + DC starts processing)
51 -> thousands (each US state then spawns a process that merges the counties, combining at the end for the whole state)
The directory structure is like this:
๐state-data/
|-๐AL.zip
|-๐AK.zip
|-๐...
|-๐WY.zip
๐county-data/
|-๐AL/
|-๐COUNTY1.csv
|-๐COUNTY2.csv
|-๐...
|-๐COUNTY68.csv
|-๐AK/
|-๐...
|-๐.../
|-๐WY/
|-๐...
Sample Data
This is extremely abbreviated, but imagine something like this:
State Level Data - 51 of these (~200 cols wide)
uid
census_plot
flood_zone
abc121
ACVB-1249575
R50
abc122
ACVB-1249575
R50
abc123
ACVB-1249575
R51
abc124
ACVB-1249599
R51
abc125
ACVB-1249599
R50
...
...
...
County Level Data - thousands of these (~300 cols wide)
uid
county
subdivision
tax_id
abc121
04021
Roland Heights
3t4g
abc122
04021
Roland Heights
3g444
abc123
04021
Roland Heights
09udd
...
...
...
...
So we join many county-level to a single state level, and thus have an aggregated, more-complete state-level data set.
Then we aggregate all the states, and we have a national level data set.
Desired Outcome
I can successfully merge one state at a time (many county to one state). I built a pipeline to do that, but the pipeline starts with a single CountyData CSV and a single StateData CSV. The issue is getting to the point where I can load the CountyData and StateData.
In other words:
#
# I need to find a way to generalize this flow to
# dynamically created COUNTY and STATE variables.
#
from apache_beam.dataframe.convert import to_pcollection
from apache_beam.dataframe.io import read_csv
COUNTY = "county-data/AL/*.csv"
STATE = "state-data/AL.zip"
def key_by_uid(elem):
return (elem.uid, elem)
with beam.Pipeline() as p:
county_df = p | read_csv(COUNTY)
county_rows_keyed = to_pcollection(county_df) | beam.Map(key_by_uid)
state_df = pd.read_csv(STATE, compression="zip")
state_rows_keys = to_pcollection(state_df, pipeline=p) | beam.Map(key_by_uid)
merged = ({ "state": state_rows_keys, "county": county_rows_keyed } ) | beam.CoGroupByKey() | beam.Map(merge_logic)
merged | WriteToParquet()
Starting with a list of states
By state, generate filepatterns to the source data
By state, load and merge the filenames
Flatten the output from each state into a US data set.
Write to Parquet file.
with beam.Pipeline(options=pipeline_options) as p:
merged_data = (
p
| beam.Create(cx.STATES)
| "PathsKeyedByState" >> tx.PathsKeyedByState()
# ('AL', {'county-data': 'gs://data/county-data/AL/COUNTY*.csv', 'state-data': 'gs://data/state-data/AL.zip'})
| "MergeSourceDataByState" >> tx.MergeSourceDataByState()
| "MergeAllStateData" >> beam.Flatten()
)
merged_data | "WriteParquet" >> tx.WriteParquet()
The issue I'm having is something like this:
I have two filepatterns in a dictionary, per state. To access those I need to use a DoFn to get at the element.
To communicate the way the data flows, I need access to Pipeline, which is a PTransform. Ex: df = p | read_csv(...)
These appear to be incompatible needs.
Here's an alternative answer.
Read the state data one at a time and flatten them, e.g.
state_dataframe = None
for state in STATES:
df = p | read_csv('/path/to/state')
df['state'] = state
if state_dataframe is None:
state_dataframe = df
else:
state_dataframe = state_dataframe.append(df)
Similarly for county data. Now join them using dataframe operations.
I'm not sure exactly what kind of merging you're doing here, but one way to structure this pipeline might be to have a DoFn that takes the county data in as a filename as an input element (i.e. you'd have a PCollection of county data filenames), opens it up using "normal" Python (e.g. pandas), and then reads the relevant state data in as a side input to do the merge.
My use case is that I want to pass the file paths or filters to a task in Airflow as an xcom so that my next task can read the data which was just processed.
Task A writes a table to a partitioned dataset and a number of Parquet file fragments are generated --> Task B reads those fragments later as a dataset. I need to only read relevant data though, not the entire dataset which could have many millions of rows.
I have tested two approaches:
List modified files right after I finish writing to the dataset. This will provide me with a list of paths which I can call ds.dataset(paths) on during my next task. I can use partitioning.parse() on these paths or check the fragments to get a list of filters used (frag.partition_expression)
A flaw with this is that I can have files being written in parallel to the same dataset.
I can generate the filters used when writing the dataset by turning the table into a pandas dataframe, doing a groupby, and then constructing filters. I am not sure if there is a simpler approach to this. I can then use pq._filters_to_expression() on the results to create a usable filter.
This is not ideal since I need to fix certain data types which do not get saved properly as an Airflow xcom (no pickling so everything has to be in json format). Also, if I want to partition on a dictionary column, I might need to tweak this function.
def create_filter_list(df, partition_columns):
"""Creates a list of pyarrow filters to be sent through an xcom and evaluated as an expression. Xcom disables pickling, so we need to save timestamp and date values as strings and convert downstream"""
filter_list = []
value_list = []
partition_keys = [df[col] for col in partition_columns]
for keys, _ in df[partition_columns].groupby(partition_keys):
if len(partition_columns) == 1:
if is_jsonable(keys):
value_list.append(keys)
elif keys is not None:
value_list.append(str(keys))
else:
if not isinstance(keys, tuple):
keys = (keys,)
read_filter = []
for name, val in zip(partition_columns, keys):
if type(val) == np.int_:
read_filter.append((name, "==", int(val)))
elif val is not None:
read_filter.append((name, "==", str(val)))
filter_list.append(read_filter)
if len(partition_columns) == 1:
if len(value_list) > 0:
filter_list = [(name, "in", value_list) for name in partition_columns]
return filter_list
Any suggestions on which approach I should take, or if there is a better way to achieve my goal?
You can watch this issue (https://issues.apache.org/jira/browse/ARROW-10440) which does what you want I believe. In the meantime, you could use basename_template as a workaround.
import glob
import os
import pyarrow as pa
import pyarrow.dataset as pads
class TrackingWriter:
def __init__(self):
self.counter = 0
part_schema = pa.schema({'part': pa.int64()})
self.partitioning = pads.HivePartitioning(part_schema)
def next_counter(self):
result = self.counter
self.counter += 1
return result
def write_dataset(self, table, base_dir):
counter = self.next_counter()
pads.write_dataset(table, base_dir, format='parquet', partitioning=self.partitioning, basename_template=f'batch-{counter}-part-{{i}}')
files_written = glob.glob(os.path.join(base_dir, '**', f'batch-{counter}-*'))
return files_written
table_one = pa.table({'part': [0, 0, 1, 1], 'val': [1, 2, 3, 4]})
table_two = pa.table({'part': [0, 0, 1, 1], 'val': [5, 6, 7, 8]})
writer = TrackingWriter()
print(writer.write_dataset(table_one, '/tmp/mydataset'))
print(writer.write_dataset(table_two, '/tmp/mydataset'))
This is just a rough sketch. You'd probably also want code to run at startup to see what the next free value of counter is. Or you could use a uuid instead of a counter.
A suggestion (not sure if this is optimal for your use case or not):
The key problem is the need to correctly select subset of the data, this can be 'fixed' upstream. The function/script that updates the big dataframe can contain a condition to save a temporary copy of data that is modified and satisfies some requirements in a separate (temporary) path. Then this file would be passed to the downstream tasks, which can delete the temporary file once it's processed.
My question is that when I run
wrk -d10s -t20 -c20 -s /mnt/c/xxxx/post.lua http://localhost:xxxx/post
the Lua script that is only executed once? It will only put one item into the database at the URL.
-- example HTTP POST script which demonstrates setting the
-- HTTP method, body, and adding a header
math.randomseed(os.time())
number = math.random()
wrk.method = "POST"
wrk.headers["Content-Type"] = "application/json"
wrk.body = '{"name": "' .. tostring(number) .. '", "title":"test","enabled":true,"defaultValue":false}'
Is there a way to make it create the 'number' variable dynamically and keep adding new items into the database until the 'wrk' command has finished its test? Or that it will keep executing the script for the duration of the test creating and inserting new 'number' variables into 'wrk.body' ?
Apologies I have literally only being looking at Lua for a few hours.
Thanks
When you do
number = math.random
you're not setting number to a random number, you're setting it equal to the function math.random. To set the variable to the value returned by the function, that line should read
number = math.random()
You may also need to set a random seed (with the math.randomseed() function and your choice of an appropriately variable argument - system time is common) to avoid math.random() giving the same result each time the script is run. This should be done before the first call to math.random.
As the script is short, system time probably isn't a good choice of seed here (the script runs far quicker than the value from os.time() changes, so running it several times immediately after one another gives the same results each time). Reading a few bytes from /dev/urandom should give better results.
You could also just use /dev/urandom to generate a number directly, rather than feeding it to math.random as a seed. Like in the code below, as taken from this answer. This isn't a secure random number generator, but for your purposes it would be fine.
urand = assert (io.open ('/dev/urandom', 'rb'))
rand = assert (io.open ('/dev/random', 'rb'))
function RNG (b, m, r)
b = b or 4
m = m or 256
r = r or urand
local n, s = 0, r:read (b)
for i = 1, s:len () do
n = m * n + s:byte (i)
end
return n
end
I have a filter optimization problem in Redis.
I have a Redis SET which keeps the doc and pos pairs of a type in a corpus.
example:
smembers type_in_docs.1
result: doc.pos pairs
array (size=216627)
0 => string '2805.2339' (length=9)
1 => string '2410.14208' (length=10)
2 => string '3516.1810' (length=9)
...
Another redis set i create live according to user choices
It contains selected docs.
smembers filteredDocs
I want to filter doc.pos pairs "type_in_docs" set according to user Doc id choices.
In fact if i didnt use concat values in set it was easy with SINTER.
So i implement a php filter code as below.
It works but need an optimization.
In big doc.pairs set too much time need. (Nearly After 150000 members!)
$concordance= $this->redis->smembers('types_in_docs.'.$typeID);
$filteredDocs= $this->redis->smembers('filteredDocs');
$filtered = array_filter($concordance, function($pairs) use ($filteredDocs) {
if( in_array(substr($pairs, 0, strpos($pairs, '.')), $filteredDocs) ) return true;
});
I tried sorted set with scores as docId.
Bu couldnt find a intersect or filter option for score values.
I am thinking and searching a Redis based solution with supported keys, sets or Lua script for time optimization.
But nothing find.
How can i filter Redis sets with concat values?
Thanks for helps.
Your code is slow primarily because you're moving a lot of data from Redis to your PHP filter. The general motivation here should be perform as much filtering as possible on the server. To do that you'd need to pay some sort of price in CPU & RAM.
There are many ways to do this, here's one:
Ensure you're using Redis v2.8.9 or above.
To allow efficiently looking for doc only, keep your doc.pos pairs as is but use Sorted Sets with score = 0, your e.g.:
ZADD type_in_docs.1 0 2805.2339 0 2410.14208 0 3516.1810
This will allow you to mimic SISMEMBER for doc in the set with:
ZRANGEBYLEX type_in_docs.1 [<$typeID> (<$typeID + "\xff">
You can now just SMEMBERS on the (usually) smaller filterDocs set and then call ZRANGEBYLEX on each for immediate gains.
If you want to do better - in extreme cases (i.e. large filterDocs, small type_in_docs) you should do the reverse.
If you want to do even better, use Lua to wrap up the filtering logic - something like:
-- #usage: redis-cli --filter_doc_pos.lua <filter set keyname> <type pairs keyname>
-- #returns: list of matching doc.pos pairs
local r = {}
for _, fv in pairs(redis.call("SMEMBERS", KEYS[1])) do
local t = redis.call("ZRANGEBYLEX", KEYS[2], "[" .. fv , "(" .. fv .. "\xff")
for _, tv in pairs(t) do
r[#r+1] = tv
end
end
return r
I am coding a survey that outputs a .csv file. Within this csv I have some entries that are space delimited, which represent multi-select questions (e.g. questions with more than one response). In the end I want to parse these space delimited entries into their own columns and create headers for them so i know where they came from.
For example I may start with this (note that the multiselect columns have an _M after them):
Q1, Q2_M, Q3, Q4_M
6, 1 2 88, 3, 3 5 99
6, , 3, 1 2
and I want to go to this:
Q1, Q2_M_1, Q2_M_2, Q2_M_88, Q3, Q4_M_1, Q4_M_2, Q4_M_3, Q4_M_5, Q4_M_99
6, 1, 1, 1, 3, 0, 0, 1, 1, 1
6,,,,3,1,1,0,0,0
I imagine this is a relatively common issue to deal with but I have not been able to find it in the R section. Any ideas how to do this in R after importing the .csv ? My general thoughts (which often lead to inefficient programs) are that I can:
(1) pull column numbers that have the special suffix with grep()
(2) loop through (or use an apply) each of the entries in these columns and determine the levels of responses and then create columns accordingly
(3) loop through (or use an apply) and place indicators in appropriate columns to indicate presence of selection
I appreciate any help and please let me know if this is not clear.
I agree with ran2 and aL3Xa that you probably want to change the format of your data to have a different column for each possible reponse. However, if you munging your dataset to a better format proves problematic, it is possible to do what you asked.
process_multichoice <- function(x) lapply(strsplit(x, " "), as.numeric)
q2 <- c("1 2 3 NA 4", "2 5")
processed_q2 <- process_multichoice(q2)
[[1]]
[1] 1 2 3 NA 4
[[2]]
[1] 2 5
The reason different columns for different responses are suggested is because it is still quite unpleasant trying to retrieve any statistics from the data in this form. Although you can do things like
# Number of reponses given
sapply(processed_q2, length)
#Frequency of each response
table(unlist(processed_q2), useNA = "ifany")
EDIT: One more piece of advice. Keep the code that processes your data separate from the code that analyses it. If you create any graphs, keep the code for creating them separate again. I've been down the road of mixing things together, and it isn't pretty. (Especially when you come back to the code six months later.)
I am not entirely sure what you trying to do respectively what your reasons are for coding like this. Thus my advice is more general โย so just feel to clarify and I will try to give a more concrete response.
1) I say that you are coding the survey on your own, which is great because it means you have influence on your .csv file. I would NEVER use different kinds of separation in the same .csv file. Just do the naming from the very beginning, just like you suggested in the second block.
Otherwise you might geht into trouble with checkboxes for example. Let's say someone checks 3 out of 5 possible answers, the next only checks 1 (i.e. "don't know") . Now it will be much harder to create a spreadsheet (data.frame) type of results view as opposed to having an empty field (which turns out to be an NA in R) that only needs to be recoded.
2) Another important question is whether you intend to do a panel survey(i.e longitudinal study asking the same participants over and over again) . That (among many others) would be a good reason to think about saving your data to a MySQL database instead of .csv . RMySQL can connect directly to the database and access its tables and more important its VIEWS.
Views really help with survey data since you can rearrange the data in different views, conditional on many different needs.
3) Besides all the personal / opinion and experience, here's some (less biased) literature to get started:
Complex Surveys: A Guide to Analysis Using R (Wiley Series in Survey Methodology
The book is comparatively simple and leaves out panel surveys but gives a lot of R Code and examples which should be a practical start.
To prevent re-inventing the wheel you might want to check LimeSurvey, a pretty decent (not speaking of the templates :) ) tool for survey conductors. Besides I TYPO3 CMS extensions pbsurvey and ke_questionnaire (should) work well too (only tested pbsurvey).
Multiple choice items should always be coded as separate variables. That is, if you have 5 alternatives and multiple choice, you should code them as i1, i2, i3, i4, i5, i.e. each one is a binary variable (0-1). I see that you have values 3 5 99 for Q4_M variable in the first example. Does that mean that you have 99 alternatives in an item? Ouch...
First you should go on and create separate variables for each alternative in a multiple choice item. That is, do:
# note that I follow your example with Q4_M variable
dtf_ins <- as.data.frame(matrix(0, nrow = nrow(<initial dataframe>), ncol = 99))
# name vars appropriately
names(dtf_ins) <- paste("Q4_M_", 1:99, sep = "")
now you have a data.frame with 0s, so what you need to do is to get 1s in an appropriate position (this is a bit cumbersome), a function will do the job...
# first you gotta change spaces to commas and convert character variable to a numeric one
y <- paste("c(", gsub(" ", ", ", x), ")", sep = "")
z <- eval(parse(text = y))
# now you assing 1 according to indexes in z variable
dtf_ins[1, z] <- 1
And that's pretty much it... basically, you would like to reconsider creating a data.frame with _M variables, so you can write a function that does this insertion automatically. Avoid for loops!
Or, even better, create a matrix with logicals, and just do dtf[m] <- 1, where dtf is your multiple-choice data.frame, and m is matrix with logicals.
I would like to help you more on this one, but I'm recuperating after a looong night! =) Hope that I've helped a bit! =)
Thanks for all the responses. I agree with most of you that this format is kind of silly but it is what I have to work with (survey is coded and going into use next week). This is what I came up with from all the responses. I am sure this is not the most elegant or efficient way to do it but I think it should work.
colnums <- grep("_M",colnames(dat))
responses <- nrow(dat)
for (i in colnums) {
vec <- as.vector(dat[,i]) #turn into vector
b <- lapply(strsplit(vec," "),as.numeric) #split up and turn into numeric
c <- sort(unique(unlist(b))) #which values were used
newcolnames <- paste(colnames(dat[i]),"_",c,sep="") #column names
e <- matrix(nrow=responses,ncol=length(c)) #create new matrix for indicators
colnames(e) <- newcolnames
#next loop looks for responses and puts indicators in the correct places
for (i in 1:responses) {
e[i,] <- ifelse(c %in% b[[i]],1,0)
}
dat <- cbind(dat,e)
}
Suggestions for improvement are welcome.