I have a large data in text files (1,000,000 lines) .Each line has 128 columns .
Here each line is a feature and each column is a dimension.
I have converted the txt files in json format and able to run sql queries on json file using spark.
Now i am trying to build a kd tree with this large data .
My steps :
1) calculate variance of each column pick the column with maximum variance and make it as key first node , mean of the column as the value of the node.
2) based on the first node value split the data into 2 parts an repeat the process until you reach a point.
my sample code :
import sqlContext._
val people = sqlContext.jsonFile("siftoutput/")
people.printSchema()
people.registerTempTable("people")
val output = sqlContext.sql("SELECT * From people")
the people table has 128 columns
My questions :
1) How to save result values of a query into a list ?
2) How to calculate variance of a column ?
3) i will be runnig multiple queries on same data .Does spark has any way to optimize it ?
4) how to save the output as key value pairs in a text file ?
please help
Related
I have a dataframe of size 3.7 million relatively small with a date column(01-01-2018 to till date) and a partner column along with other unique ids.I want to write the data frame to s3 location by partitioning it by date first and then partner(5 partners for instance P1,P2,P3,P4 and P5). Below is my schema and code
df schema is
id1: long
id2: long
id3: long
partner: string
dt: date
df = df1.select('dt','partner').distinct().groupBy('partner').agg(F.collect_set('dt').alias('dt'))
dummy_list = []
for i in df.collect():
dummy_list.append(i.partner)
for src in dummy_list:
for dt1 in i.dt:
df.filter(F.col('dt') == dt1).filter(F.col('partner') == src).write.mode("overwrite").parquet("s3://test/parquet/dt={}/partner={}".format(datetime.strftime(dt1,'%Y-%m-%d'),src))
the above code runs successfully but it is taking more than 4-5 hours(i cancelled it midway) to write the dataframe in the s3 location. Any ways I can reduce the time significantly? Can anyone help me validate the code or correct the code if necessary in order to achieve this faster. I am new to this, appreciate any help.
Sample Data
id1|id2|id3|partner|dt
100|200|300|p1 |01-01-2018
101|200|30 |p2 |01-01-2020
102|202|311|p3 |01-01-2019
103|201|320|p4 |01-02-2019
104|210|305|p5 |01-03-2018
.
.
.
I'm trying to perform calculations based on the entries in a pandas dataframe. The dataframe looks something like this:
and it contains 1466 rows. I'll have to run similar calculations on other dfs with more rows later.
What I'm trying to do, is calculate something like mag='(U-V)/('R-I)' (but ignoring any values that are -999), put that in a new column, and then z_pred=10**((mag-c)m) in a new column (mag, c and m are just hard-coded variables). I have other columns I need to add too, but I figure that'll just be an extension of the same method.
I started out by trying
for i in range(1):
current = qso[:]
mag = (U-V)/(R-I)
name = current['NED']
z_pred = 10**((mag - c)/m)
z_meas = current['z']
but I got either a Series for z, which I couldn't operate on, or various type errors when I tried to print the values or write them to a file.
I found this question which gave me a start, but I can't see how to apply it to multiple calculations, as in my situation.
How can I achieve this?
Conditionally adding calculated columns row wise are usually performed with numpy's np.where;
df['mag'] = np.where(~df[['U', 'V', 'R', 'I']].eq(-999).any(1), (df.U - df.V) / (df.R - df.I), -999)
Note; assuming here that when any of the columns contain '-999' it will not be calculated and a '-999' is returned.
There is a huge file, almost reaching 10 million rows, which I would like to split based on the values of a given column. One represents internal measurements (inside a house) and the other one is the external data (outside). This codes takes way too long to split, any ideas?
fext = open('external.csv', 'a')
fint = open('internal.csv', 'a')
for df in pd.read_csv('todo.csv', parse_dates=['Measured At'],
low_memory=False, chunksize=500000):
dfExt = df[df['Temperatura Exterior'].notnull()]
dfInt = df[df['Temperatura Exterior'].isnull()]
dfExt.to_csv(fext,header=False)
dfInt.to_csv(fint,header=False)
fext.close();fint.close()
I'm trying to read a csv file where metadata is placed on row 4 to 7
Source,"EUROSTAT","INE","BDP"
Magnitude,,,
Unit ,"Percent","Percent","Unit"
Series,"Rate","PIB","Growth"
Where the relevant data starts on row 10. This CSV format will always be fixed, with this sort of data and this row disposition
Date,Series1,Series2,Series3
30-09-2014,13.1,1.1,5.55
30-06-2014,13.9,0.9,5.63
31-03-2014,15.1,1.0,5.57
31-12-2013,15.3,1.6,5.55
30-09-2013,15.5,-1.0,5.66
30-06-2013,16.4,-2.1,5.65
What I've done was skip the first rows and read the data from Row 10 afterwards, and define the column metadata myself. However, the rows don't have the Unit type nor the magnitude that was defined in the Metadata header. In SSIS I do an Unpivot of the columns to an Indicator column so I have the Date, Series, Value style of table.
My question here is how can I make a table of the format Date, Series, Value, Magnitude, Unit where the Series, Magnitude, Unit are read from the first 10 rows?
I have 48 matrices of dimensions 1,000 rows and 300,000 columns where each column has a respective ID, and each row is a measurement at one time point. Each of the 48 matrices is of the same dimension and their column IDs are all the same.
The way I have the matrices stored now is as RData objects and also as text files. I guess for SQL I'd have to transpose and store by ID, and in such case now the matrix would be of dimensions 300,000 rows and 1,000 columns.
I guess if I transpose it a small version of the data would look like this:
id1 1.5 3.4 10 8.6 .... 10 (with 1,000 columns, and 30,0000 rows now)
I want to store them in a way such that I can use R to retrieve a few of the rows (~ 5 to 100 each time).
The general strategy I have in mind is as follows:
(1) Create a database in sqlite3 using R that I will use to store the matrices (in different tables)
For file 1 to 48 (each file is of dim 1,000 rows and 300,000 columns):
(2) Read in file into R
(3) Store the file as a matrix in R
(4) Transpose the matrix (now its of dimensions 300,000 rows and 1,000 columns). Each row now is the unique id in the table in sqlite.
(5) Dump/write the matrix into the sqlite3 database created in (1) (dump it into a new table probably?)
Steps 1-5 are to create the DB.
Next, I need step 6 to read-in the database:
(6) Read some rows (at most 100 or so at a time) into R as a (sub)matrix.
A simple example code doing steps 1-6 would be best.
Some Thoughts:
I have used SQL before but it was mostly to store tabular data where each column had a name, in this case each column is just one point of the data matrix, I guess I could just name it col1 ... to col1000? or there are better tricks?
If I look at: http://sandymuspratt.blogspot.com/2012/11/r-and-sqlite-part-1.html they show this example:
dbSendQuery(conn = db,
"CREATE TABLE School
(SchID INTEGER,
Location TEXT,
Authority TEXT,
SchSize TEXT)")
But in my case this would look like:
dbSendQuery(conn = db,
"CREATE TABLE mymatrixdata
(myid TEXT,
col1 float,
col2 float,
.... etc.....
col1000 float)")
I.e., I have to type in col1 to ... col1000 manually, that doesn't sound very smart. This is where I am mostly stuck. Some code snippet would help me.
Then, I need to dump the text files into the SQLite database? Again, unsure how to do this from R.
Seems I could do something like this:
setwd(<directory where to save the database>)
db <- dbConnect(SQLite(), dbname="myDBname")
mymatrix.df = read.table(<full name to my text file containing one of the matrices>)
mymatrix = as.matrix(mymatrix.df)
Here I need to now the coe on how to dump this into the database...
Finally,
How to fast retrieve the values (without having to read the entire matrices each time) for some of the rows (by ID) using R?
From the tutorial it'd look like this:
sqldf("SELECT id1,id2,id30 FROM mymatrixdata", dbname = "Test2.sqlite")
But it the id1,id2,id30 are hardcoded in the code and I need to dynamically obtain them. I.e., sometimes i may want id1, id2, id10, id100; and another time i may want id80, id90, id250000, etc.
Something like this would be more approp for my needs:
cols.i.want = c("id1","id2","id30")
sqldf("SELECT cols.i.want FROM mymatrixdata", dbname = "Test2.sqlite")
Again, unsure how to proceed here. Code snippets would also help.
A simple example would help me a lot here, no need to code the whole 48 files, etc. just a simple example would be great!
Note: I am using Linux server, SQlite 3 and R 2.13 (I could update it as well).
In the comments the poster explained that it is only necessary to retrieve specific rows, not columns:
library(RSQLite)
m <- matrix(1:24, 6, dimnames = list(LETTERS[1:6], NULL)) # test matrix
con <- dbConnect(SQLite()) # could add dbname= arg. Here use in-memory so not needed.
dbWriteTable(con, "m", as.data.frame(m)) # write
dbGetQuery(con, "create unique index mi on m(row_names)")
# retrieve submatrix back as m2
m2.df <- dbGetQuery(con, "select * from m where row_names in ('A', 'C')
order by row_names")
m2 <- as.matrix(m2.df[-1])
rownames(m2) <- m2.df$row_names
Note that relational databases are set based and the order that the rows are stored in is not guaranteed. We have used order by row_names to get out a specific order. If that is not good enough then add a column giving the row index: 1, 2, 3, ... .
REVISED based on comments.