Develop Reference

Making comparing 2 tables faster (Postgres/SQLAlchemy)

I wrote a code in python to manipulate a table I have in my database. I am doing so using SQL Alchemy. Basically I have table 1 that has 2 500 000 entries. I have another table 2 with 200 000 entries. Basically what I am trying to do, is compare my source ip and dest ip in table 1 with source ip and dest ip in table 2. if there is a match, I replace the ip source and ip dest in table 1 with a data that matches ip source and ip dest in table 2 and I add the entry in table 3. My code also checks if the entry isn't already in the new table. If so, it skips it and then goes on with the next row.
My problem is its extremely slow. I launched my script yesterday and in 24 hours it only went through 47 000 entries out of 2 500 000. I am wondering if there are anyways I can speed up the process. It's a postgres db and I can't tell if the script taking this much time is reasonable or if something is up. If anyone had a similar experience with something like this, how much time did it take before completion ?
Many thanks.
session = Session()
i = 0
start_id = 1
flows = session.query(Table1).filter(Table1.id >= start_id).all()
result_number = len(flows)
vlan_list = {"['0050']", "['0130']", "['0120']", "['0011']", "['0110']"}
while i < result_number:
for flow in flows:
if flow.vlan_destination in vlan_list:
usage = session.query(Table2).filter(Table2.ip ==
str(flow.ip_destination)).all()
if len(usage) > 0:
usage = usage[0].usage
else:
usage = str(flow.ip_destination)
usage_ip_src = session.query(Table2).filter(Table2.ip ==
str(flow.ip_source)).all()
if len(usage_ip_src) > 0:
usage_ip_src = usage_ip_src[0].usage
else:
usage_ip_src = str(flow.ip_source)
if flow.protocol == "17":
protocol = func.REPLACE(flow.protocol, "17", 'UDP')
elif flow.protocol == "1":
protocol = func.REPLACE(flow.protocol, "1", 'ICMP')
elif flow.protocol == "6":
protocol = func.REPLACE(flow.protocol, "6", 'TCP')
else:
protocol = flow.protocol
is_in_db = session.query(Table3).filter(Table3.protocol ==
protocol)\
.filter(Table3.application == flow.application)\
.filter(Table3.destination_port == flow.destination_port)\
.filter(Table3.vlan_destination == flow.vlan_destination)\
.filter(Table3.usage_source == usage_ip_src)\
.filter(Table3.state == flow.state)\
.filter(Table3.usage_destination == usage).count()
if is_in_db == 0:
to_add = Table3(usage_ip_src, usage, protocol, flow.application, flow.destination_port,
flow.vlan_destination, flow.state)
session.add(to_add)
session.flush()
session.commit()
print("added " + str(i))
else:
print("usage already in DB")
i = i + 1
session.close()
EDIT As requested, here are more details : Table 1 has 11 columns, the two we are interested in are source ip and dest ip.
Table 1
Here, I have Table 2 :Table 2. It has an IP and a Usage. What my script is doing is that it takes source ip and dest ip from table one and looks up if there is a match in Table 2. If so, it replaces the ip address by usage, and adds this along with some of the columns of Table 1 in Table 3 :[Table3][3]
Along doing this, when adding the protocol column into Table 3, it writes the protocol name instead of the number, just to make it more readable.
EDIT 2 I am trying to think about this differently, so I did a diagram of my problem Diagram (X problem)
What I am trying to figure out is if my code (Y solution) is working as intended. I've been coding in python for a month only and I feel like I am messing something up. My code is supposed to take every row from my Table 1, compare it to Table 2 and add data to table 3. My Table one has over 2 million entries and it's understandable that it should take a while but its too slow. For example, when I had to load the data from the API to the db, it went faster than the comparisons im trying to do with everything that is already in the db. I am running my code on a virtual machine that has sufficient memory so I am sure it's my code that is lacking and I need direction to as what can be improved. Screenshots of my tables:
Table 2
Table 3
Table 1
EDIT 3 : Postgresql QUERY
SELECT
coalesce(table2_1.usage, table1.ip_source) AS coalesce_1,
coalesce(table2_2.usage, table1.ip_destination) AS coalesce_2,
CASE table1.protocol WHEN %(param_1) s THEN %(param_2) s WHEN %(param_3) s THEN %(param_4) s WHEN %(param_5) s THEN %(param_6) s ELSE table1.protocol END AS anon_1,
table1.application AS table1_application,
table1.destination_port AS table1_destination_port,
table1.vlan_destination AS table1_vlan_destination,
table1.state AS table1_state
FROM
table1
LEFT OUTER JOIN table2 AS table2_2 ON table2_2.ip = table1.ip_destination
LEFT OUTER JOIN table2 AS table2_1 ON table2_1.ip = table1.ip_source
WHERE
table1.vlan_destination IN (
%(vlan_destination_1) s,
%(vlan_destination_2) s,
%(vlan_destination_3) s,
%(vlan_destination_4) s,
%(vlan_destination_5) s
)
AND NOT (
EXISTS (
SELECT
1
FROM
table3
WHERE
table3.usage_source = coalesce(table2_1.usage, table1.ip_source)
AND table3.usage_destination = coalesce(table2_2.usage, table1.ip_destination)
AND table3.protocol = CASE table1.protocol WHEN %(param_1) s THEN %(param_2) s WHEN %(param_3) s THEN %(param_4) s WHEN %(param_5) s THEN %(param_6) s ELSE table1.protocol END
AND table3.application = table1.application
AND table3.destination_port = table1.destination_port
AND table3.vlan_destination = table1.vlan_destination
AND table3.state = table1.state
)
)

Given the current question, I think this at least comes close to what you might be after. The idea is to perform the entire operation in the database, instead of fetching everything – the whole 2,500,000 rows – and filtering in Python etc.:
from sqlalchemy import func, case
from sqlalchemy.orm import aliased
def newhotness(session, vlan_list):
# The query needs to join Table2 twice, so it has to be aliased
dst = aliased(Table2)
src = aliased(Table2)
# Prepare required SQL expressions
usage = func.coalesce(dst.usage, Table1.ip_destination)
usage_ip_src = func.coalesce(src.usage, Table1.ip_source)
protocol = case({"17": "UDP",
"1": "ICMP",
"6": "TCP"},
value=Table1.protocol,
else_=Table1.protocol)
# Form a query producing the data to insert to Table3
flows = session.query(
usage_ip_src,
usage,
protocol,
Table1.application,
Table1.destination_port,
Table1.vlan_destination,
Table1.state).\
outerjoin(dst, dst.ip == Table1.ip_destination).\
outerjoin(src, src.ip == Table1.ip_source).\
filter(Table1.vlan_destination.in_(vlan_list),
~session.query(Table3).
filter_by(usage_source=usage_ip_src,
usage_destination=usage,
protocol=protocol,
application=Table1.application,
destination_port=Table1.destination_port,
vlan_destination=Table1.vlan_destination,
state=Table1.state).
exists())
stmt = insert(Table3).from_select(
["usage_source", "usage_destination", "protocol", "application",
"destination_port", "vlan_destination", "state"],
flows)
return session.execute(stmt)
If the vlan_list is selective, or in other words filters out most rows, this will perform a lot less operations in the database. Depending on the size of Table2 you may benefit from indexing Table2.ip, but do test first. If it is relatively small, I would guess that PostgreSQL will perform a hash or nested loop join there. If some column of the ones used to filter out duplicates in Table3 is unique, you could perform an INSERT ... ON CONFLICT ... DO NOTHING instead of removing duplicates in the SELECT using the NOT EXISTS subquery expression (which PostgreSQL will perform as an antijoin). If there is a possibility that the flows query may produce duplicates, add a call to Query.distinct() to it.

Quickly count the number of objects in bson document

I'd like to calculated the number of documents stored in a mongodb bson file without having to import the file into the db via mongo restore.
The best I've been able to come up with in python is
bson_doc = open('./archive.bson','rb')
it = bson.decode_file_iter(bson_doc)
total = sum(1 for _ in it)
print(total)
This works in theory, but is slow in practice when bson documents are large. Anyone have a quicker approach to counting the number of documents in a bson document without doing a full decode?
I am currently using the python 2.7 and pymongo.
https://api.mongodb.com/python/current/api/bson/index.html

I don't have a file at hand to try, but I believe there's a way - if you'll parse the data by hand.
The source for bson.decode_file_iter (sans the docstring) goes like this:
_UNPACK_INT = struct.Struct("<i").unpack
def decode_file_iter(file_obj, codec_options=DEFAULT_CODEC_OPTIONS):
while True:
# Read size of next object.
size_data = file_obj.read(4)
if len(size_data) == 0:
break # Finished with file normaly.
elif len(size_data) != 4:
raise InvalidBSON("cut off in middle of objsize")
obj_size = _UNPACK_INT(size_data)[0] - 4
elements = size_data + file_obj.read(obj_size)
yield _bson_to_dict(elements, codec_options)
I presume, the time-consuming operation is _bson_to_dict call - and you don't need one.
So, all you need is to read the file - get the int32 value with the next document's size and skip it. Then count how many documents you've encountered doing this.
So, I believe, this function should do the trick:
import struct
import os
from bson.errors import InvalidBSON
def count_file_documents(file_obj):
"""Counts how many documents provided BSON file contains"""
cnt = 0
while True:
# Read size of next object.
size_data = file_obj.read(4)
if len(size_data) == 0:
break # Finished with file normaly.
elif len(size_data) != 4:
raise InvalidBSON("cut off in middle of objsize")
obj_size = struct.Struct("<i").unpack(size_data)[0] - 4
# Skip the next obj_size bytes
file_obj.seek(obj_size, os.SEEK_CUR)
cnt += 1
return cnt
(I haven't tested the code, though. Don't have MongoDB at hand.)

Large Data Analytics

I'm trying to analyze a large amount of GitHub Archive Data and am stumped by many limitations.
So my analysis requires me too search a 350GB Data set. I have a local copy of the data and there is also a copy available via Google BigQuery. The local dataset is split up into 25000 individual files. The dataset is a timeline of events.
I want to plot the number of stars each repository has since its creation. (Only for repos with > 1000 currently)
I can get this result very quickly using Google BigQuery, but it "analyzes" 13.6GB of data each time. This limits me to <75 requests without having to pay $5 per additional 75.
My other option is to search through my local copy, but searching through each file for a specific string (repository name) takes way too long. Took over an hour on an SSD drive to get through half the files before I killed the process.
What is a better way I can approach analyzing such a large amount of data?
Python Code for Searching Through all Local Files:
for yy in range(11,15):
for mm in range(1,13):
for dd in range(1,32):
for hh in range(0,24):
counter = counter + 1
if counter < startAt:
continue
if counter > stopAt:
continue
#print counter
strHH = str(hh)
strDD = str(dd)
strMM = str(mm)
strYY = str(yy)
if len(strDD) == 1:
strDD = "0" + strDD
if len(strMM) == 1:
strMM = "0" + strMM
#print strYY + "-" + strMM + "-" + strDD + "-" + strHH
try:
f = json.load (open ("/Volumes/WD_1TB/GitHub Archive/20"+strYY+"-"+strMM+"-"+strDD+"-"+strHH+".json", 'r') , cls=ConcatJSONDecoder)
for each_event in f:
if(each_event["type"] == "WatchEvent"):
try:
num_stars = int(each_event["repository"]["watchers"])
created_at = each_event["created_at"]
json_entry[4][created_at] = num_stars
except Exception, e:
print e
except Exception, e:
print e
Google Big Query SQL Command:
SELECT repository_owner, repository_name, repository_watchers, created_at
FROM [githubarchive:github.timeline]
WHERE type = "WatchEvent"
AND repository_owner = "mojombo"
AND repository_name = "grit"
ORDER BY created_at
I am really stumped so any advice at this point would be greatly appreciated.

If most of your BigQuery queries only scan a subset of the data, you can do one initial query to pull out that subset (use "Allow Large Results"). Then subsequent queries against your small table will cost less.
For example, if you're only querying records where type = "WatchEvent", you can run a query like this:
SELECT repository_owner, repository_name, repository_watchers, created_at
FROM [githubarchive:github.timeline]
WHERE type = "WatchEvent"
And set a destination table as well as the "Allow Large Results" flag. This query will scan the full 13.6 GB, but the output is only 1 GB, so subsequent queries against the output table will only charge you for 1 GB at most.
That still might not be cheap enough for you, but just throwing the option out there.

I found a solution to this problem - Using a database. i imported the relevant data from my 360+GB of JSON data to a MySQL Database and queried that instead. What used to be a 3hour+ query time per element became <10seconds.
MySQL wasn't the easiest thing to set up, and import took approximately ~7.5 hours, but the results made it well worth it for me.

How do I change ADO ResultSet format in python?

I have the following code to query a database using an ADO COMObject in python. This is connecting to a Time series database (OSIPI) and this is the only way we've been able to get Python connected to the database.
from win32com.client import Dispatch
oConn = Dispatch('ADODB.Connection')
oRS = Dispatch('ADODB.RecordSet')
oConn.ConnectionString = <my connection string>
oConn.Open()
oRS.ActiveConnection = oConn
if oConn.State == adStateOpen:
print "Connected to DB"
else:
raise SystemError('Database Connection Failed')
cmd = """SELECT tag, dataowner FROM pipoint WHERE tag LIKE 'TEST_TAG1%'"""
self.oRS.Open(cmd)
result = oRS.GetRows(1)
print result
result2 = oRS.GetRows(2)
print result2
if oConn.State == adStateOpen:
oConn.Close()
oConn = None
This code returns the following two lines as results to the query:
result ((u'TEST_TAG1.QTY.BLACK',), (u'piadmin',))
result2 = ((u'TEST_TAG1.QTY.BLACK', u'TEST_TAG1.QTY.PINK'), (u'piadmin', u'piuser'))
This is not the expected format. In this case, I was expecting something like this:
result = ((u'TEST_TAG1.QTY.BLACK',u'piadmin'))
result2 = ((u'TEST_TAG1.QTY.BLACK',u'piadmin'),
(u'TEST_TAG1.QTY.PINK',u'piuser'))
Is there a way to adjust the results of an ADO query so everything related to row 1 is in the same tuple and everything in row 2 is in the same tuple?

What you're seeing is not really a Python thing but the output of GetRows(), which returns a two-dimensional array, which is organized by by field and then row.
Fortunately, Python has the zip() function that will make the relevant change for you. Try changing your code from:
result = oRS.GetRows(1)
to:
result = zip(*oRS.GetRows(1))
etc.

python mysqldb multiple connections

Hey guys,
i have the following problem:
1 process executes a very large query and writes the results to a file, inbetween the process should update an status to the database.
first thaught: NO PROBLEM, pseudo code:
db = mysqldb.connect()
cursor = db.cursor()
large = cursor.execute(SELECT * FROM VERYLARGETABLE)
for result in large.fetchall():
file.write(result)
if timetoUpdateStatus: cursor.execute(UPDATE STATUS)
problem: when getting 9 million results the "large = cursor.execute(SELECT * FROM VERYLARGETABLE)" never finishes... i figured out a border at 2 million entrys at 4 columns where the mysql server finished the query after 30 seconds but the python process keeps running for hours... that maybe a bug in the Python MySQLDB library..
SO SECOND TRY: db.query function with db.use_results() and fetch_row():
db = mysqldb.connect()
cursor = db.cursor()
db.query(SELECT * FROM VERYLARGETABLE)
large = large.use_result()
while true:
for row in large.fetch_row(100000):
file.write(row)
if timetoUpdateStatus: cursor.execute(UPDATE STATUS) <-- ERROR (2014, "Commands out of sync; you can't run this command now")
so THIRD TRY was using 2 MySQL connections... which doesnt work, when i open a second connection the first one disappears....
any suggestions??

Try using a MySQL SSCursor. It will keep the result set in the server (MySQL data structure), rather than transfer the result set to the client (Python data structure) which is what the default cursor does. Using an SSCursor will avoid the long initial delay caused by the default cursor trying to build a Python data structure -- and allocate memory for -- the huge result set. Thus, the SSCursor should also require less memory.
import MySQLdb
import MySQLdb.cursors
import config
cons = [MySQLdb.connect(
host=config.HOST, user=config.USER,
passwd=config.PASS, db=config.MYDB,
cursorclass=MySQLdb.cursors.SSCursor) for i in range(2)]
select_cur, update_cur = [con.cursor() for con in cons]
select_cur.execute(SELECT * FROM VERYLARGETABLE)
for i, row in enumerate(select_cur):
print(row)
if i % 100000 == 0 or timetoUpdateStatus:
update_cur.execute(UPDATE STATUS)

Try splitting up the "select * from db" query into smaller chunks
index=0
while True:
cursor.execute('select * from verylargetable LIMIT %s,%s', (index, index+10000))
records = cursor.fetchall()
if len(records)==0:
break
file.write(records)
index+=10000
file.close()

Use the LIMIT statement in your big select:
limit = 0
step = 10000
query = "SELECT * FROM VERYLARGETABLE LIMIT %d, %d"
db = mysqldb.connect()
cursor = db.cursor()
while true:
cursor.execute(query, (step, limit))
for row in cursor.fetch_all():
file.write(row)
if timetoUpdateStatus:
cursor.execute(update_query)
limit += step
Code is not tested, but you should get the idea.