提交 70297a46 编写于 作者: A Alex Duan

test: first submit udf python case

上级 83ac6ae0
import pickle
def init():
pass
def destroy():
pass
def start():
return pickle.dumps([])
def finish(buf):
mins = pickle.loads(buf)
min_val = None
for min in mins:
if min < min_val:
min_val = min
return min_val
def reduce(datablock, buf):
(rows, cols) = datablock.shape()
mins = pickle.loads(buf)
min = None
for i in range(rows):
val = datablock.data(i, 0)
if min is None or (val is not None and val < min) :
min = val
if min is not None:
mins.append(min)
return pickle.dumps(mins)
import pickle
def init():
pass
def destroy():
pass
def start():
return pickle.dumps([])
def finish(buf):
return None
def reduce(datablock, buf):
(rows, cols) = datablock.shape()
mins = pickle.loads(buf)
mins.append(None)
return pickle.dumps(mins)
import pickle
def init():
pass
def destroy():
pass
def start():
return pickle.dumps([])
def finish(buf):
sums = pickle.loads(buf)
all = 0
for sum in sums:
all += sum
return all
def reduce(datablock, buf):
(rows, cols) = datablock.shape()
sums = pickle.loads(buf)
sum = 0
for i in range(rows):
val = datablock.data(i, 0)
if val is not None:
sum += val
sums.append(sum)
return pickle.dumps(sums)
# init
def init():
pass
# destory
def destory():
pass
# return origin column one value
def process(block):
(nrows, ncols) = block.shape()
results = []
for i in range(nrows):
rows = []
for j in range(ncols):
val = block.data(i, j)
rows.append(val)
results.append(','.join(rows))
return results
# init
def init():
pass
# destory
def destory():
pass
# return origin column one value
def process(block):
(rows, cols) = block.shape()
results = []
for i in range(rows):
results.append(None)
return results
\ No newline at end of file
# init
def init():
pass
# destory
def destory():
pass
# return origin column one value
def process(block):
(rows, cols) = block.shape()
results = []
for i in range(rows):
results.append(block.data(i,0))
return results
###################################################################
# Copyright (c) 2016 by TAOS Technologies, Inc.
# All rights reserved.
#
# This file is proprietary and confidential to TAOS Technologies.
# No part of this file may be reproduced, stored, transmitted,
# disclosed or used in any form or by any means other than as
# expressly provided by the written permission from Jianhui Tao
#
###################################################################
# -*- coding: utf-8 -*-
from util.log import *
from util.cases import *
from util.sql import *
from util.common import *
from util.sqlset import *
import random
import os
class TDTestCase:
def init(self, conn, logSql, replicaVar=1):
self.replicaVar = int(replicaVar)
tdLog.debug("start to execute %s" % __file__)
tdSql.init(conn.cursor())
self.setsql = TDSetSql()
# udf path
self.udf_path = os.path.dirname(os.path.realpath(__file__)) + "/udfpy"
self.column_dict = {
'ts': 'timestamp',
'col1': 'tinyint',
'col2': 'smallint',
'col3': 'int',
'col4': 'bigint',
'col5': 'tinyint unsigned',
'col6': 'smallint unsigned',
'col7': 'int unsigned',
'col8': 'bigint unsigned',
'col9': 'float',
'col10': 'double',
'col11': 'bool',
'col12': 'varchar(20)',
'col13': 'nchar(20)',
'col14': 'timestamp'
}
self.tag_dict = {
't1': 'tinyint',
't2': 'smallint',
't3': 'int',
't4': 'bigint',
't5': 'tinyint unsigned',
't6': 'smallint unsigned',
't7': 'int unsigned',
't8': 'bigint unsigned',
't9': 'float',
't10': 'double',
't11': 'bool',
't12': 'varchar(20)',
't13': 'nchar(20)',
't14': 'timestamp'
}
def set_stb_sql(self,stbname,column_dict,tag_dict):
column_sql = ''
tag_sql = ''
for k,v in column_dict.items():
column_sql += f"{k} {v}, "
for k,v in tag_dict.items():
tag_sql += f"{k} {v}, "
create_stb_sql = f'create stable {stbname} ({column_sql[:-2]}) tags ({tag_sql[:-2]})'
return create_stb_sql
# create stable and child tables
def create_table(self, stbname, tbname, count):
tdSql.prepare()
tdSql.execute('use db')
self.child_count = count
self.stbname = stbname
self.tbname = tbname
# create stable
create_table_sql = self.set_stb_sql(stbname, self.column_dict, self.tag_dict)
tdSql.execute(create_table_sql)
# create child table
for i in range(count):
ti = i % 128
tags = f'{ti},{ti},{i},{i},{ti},{ti},{i},{i},{i}.000{i},{i}.000{i},true,"var{i}","nch{i}",now'
sql = f'create table {tbname}{i} using {stbname} tags({tags})'
tdSql.execute(sql)
tdLog.info(f" create {count} child tables ok.")
def create_udfpy_impl(self, funs, filename):
for name, outtype in funs.items():
sql = f' create function {name} as "{self.udf_path}/{filename} {outtype} " language "Python" '
tdSql.execute(sql)
def create_udfpy_dicts(self, dicts, filename):
for k,v in dicts:
self.create_udfpy_impl(k, v, filename)
# create_udfpy_function
def create_udfpy_function(self):
# function
# scalar funciton
self.scalar_funs = {
'sf1': 'tinyint',
'sf2': 'smallint',
'sf3': 'int',
'sf4': 'bigint',
'sf5': 'tinyint unsigned',
'sf6': 'smallint unsigned',
'sf7': 'int unsigned',
'sf8': 'bigint unsigned',
'sf9': 'float',
'sf10': 'double',
'sf11': 'bool',
'sf12': 'varchar(20)',
'sf13': 'nchar(20)',
'sf14': 'timestamp'
}
# agg function
self.agg_funs = {
'af1': 'tinyint',
'af2': 'smallint',
'af3': 'int',
'af4': 'bigint',
'af5': 'tinyint unsigned',
'af6': 'smallint unsigned',
'af7': 'int unsigned',
'af8': 'bigint unsigned',
'af9': 'float',
'af10': 'double',
'af11': 'bool',
'af12': 'varchar(20)',
'af13': 'nchar(20)',
'af14': 'timestamp'
}
# files
self.create_udfpy_function(self.scalar_funs, "fun_origin")
self.create_udf_sf("sf_multi_args", "binary(1024)")
#self.create_udfpy_function(self.agg_funs, None)
def create_udf_sf(self, fun_name, out_type):
sql = f'create function {fun_name} as {self.udf_path}{fun_name}.py {out_type} language "Python"'
tdSql.execute(sql)
def create_udf_af(self, fun_name, out_type, bufsize):
sql = f'create aggregate function {fun_name} as {self.udf_path}{fun_name}.py {out_type} bufsize {bufsize} language "Python"'
tdSql.execute(sql)
# sql1 query result eual with sql2
def verify_same_result(self, sql1, sql2):
# query
result1 = tdSql.getResult(sql1)
tdSql.query(sql2)
for i, row in enumerate(result1):
for j , val in enumerate(row):
tdSql.checkData(i, j, result1[i][j])
# same value like select col1, udf_fun1(col1) from st
def verfiy_same_value(sql):
tdSql.query(sql)
nrows = tdSql.getRows()
for i in range(nrows):
val = tdSql.getData(i, 0)
tdSql.checkData(i, 1, val)
# verify multi values
def verify_same_multi_values(self, sql):
tdSql.query(sql)
nrows = tdSql.getRows()
for i in range(nrows):
udf_val = tdSql.getData(i, 0)
vals = udf_val.split(',')
for j,val in enumerate(vals, 1):
tdSql.checkData(i, j, val)
# query multi-args
def query_multi_args(self):
cols = self.column_dict.keys() + self.tag_dict.keys()
ncols = len(cols)
for i in range(2, ncols):
sample = random.sample(i)
cols_name = ','.join(sample)
sql = f'select sf_multi_args({cols_name}),{cols_name} from {self.stbname}'
self.verify_same_multi_values(sql)
# query_udfpy
def query_scalar_udfpy(self):
# col
for col_name, col_type in self.column_dict:
for fun_name, out_type in self.scalar_funs:
sql = f'select {col_name} {fun_name}({col_name}) from {self.stbname}'
self.verify_same_value(sql)
# multi-args
self.query_multi_args()
# create aggregate
def create_aggr_udfpy(self):
# all type check null
for col_name, col_type in self.column_dict:
self.create_udf_af(f"af_null_{col_name}", f"{col_type}", 10*1024*1024)
# min
self.create_udf_af(f"af_min_float", f"float", 10*1024*1024)
self.create_udf_af(f"af_min_int", f"int", 10*1024*1024)
# sum
self.create_udf_af(f"af_sum_float", f"float", 100*1024*1024)
self.create_udf_af(f"af_sum_int", f"sum", 100*1024*1024)
# query aggregate
def query_aggr_udfpy(self) :
# all type check null
for col_name, col_type in self.column_dict:
fun_name = f"af_null_{col_name}"
sql = f'select {fun_name}(col_name) from {self.stbname}'
tdSql.query(sql)
tdSql.checkData(0, 0, "NULL")
# min
sql = f'select min(col3), af_min_int(col3) from {self.stbname}'
self.verfiy_same_value(sql)
sql = f'select min(col7), af_min_int(col7) from {self.stbname}'
self.verfiy_same_value(sql)
sql = f'select min(col9), af_min_float(col9) from {self.stbname}'
self.verfiy_same_value(sql)
# sum
sql = f'select sum(col3), af_sum_int(col3) from {self.stbname}'
self.verfiy_same_value(sql)
sql = f'select sum(col7), af_sum_int(col7) from {self.stbname}'
self.verfiy_same_value(sql)
sql = f'select sum(col9), af_sum_float(col9) from {self.stbname}'
self.verfiy_same_value(sql)
# insert to child table d1 data
def insert_data(self, tbname, rows):
ts = 1670000000000
for i in range(self.child_count):
for j in range(rows):
ti = j % 128
cols = f'{ti},{ti},{i},{i},{ti},{ti},{i},{i},{i}.000{i},{i}.000{i},true,"var{i}","nch{i}",now'
sql = f'insert into {tbname}{i} values({ts+j},{cols});'
tdSql.execute(sql)
tdLog.info(f" insert {rows} for each child table.")
# run
def run(self):
# var
stable = "meters"
tbname = "d"
count = 100
# do
self.create_table(stable, tbname, count)
self.insert_data(tbname, 1000)
# scalar
self.create_scalar_udfpy()
self.query_scalar_udfpy()
# aggregate
self.create_aggr_udfpy()
self.query_aggr_udfpy()
def stop(self):
tdSql.close()
tdLog.success("%s successfully executed" % __file__)
tdCases.addWindows(__file__, TDTestCase())
tdCases.addLinux(__file__, TDTestCase())
\ No newline at end of file
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