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s3-tests/s3tests_boto3/functional/test_s3select.py
gal salomon fce9a52ef4 add filter for s3select tests 
Signed-off-by: gal salomon <gal.salomon@gmail.com>
2020-07-07 00:10:50 +03:00

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import nose
import random
from nose.plugins.attrib import attr
from . import (
get_client
)
region_name = ''
# recurssion function for generating arithmetical expression
def random_expr(depth):
# depth is the complexity of expression
if depth==1 :
return str(int(random.random() * 100) + 1)+".0"
return '(' + random_expr(depth-1) + random.choice(['+','-','*','/']) + random_expr(depth-1) + ')'
def generate_s3select_where_clause(bucket_name,obj_name):
a=random_expr(4)
b=random_expr(4)
s=random.choice([ '<','>','==','<=','>=','!=' ])
try:
eval( a )
eval( b )
except ZeroDivisionError:
return
# generate s3select statement using generated randome expression
# upon count(0)>0 it means true for the where clause expression
# the python-engine {eval( conditional expression )} should return same boolean result.
s3select_stmt = "select count(0) from stdin where " + a + s + b + ";"
res = remove_xml_tags_from_result( run_s3select(bucket_name,obj_name,s3select_stmt) ).replace(",","")
nose.tools.assert_equal(int(res)>0 , eval( a + s + b ))
def generate_s3select_expression_projection(bucket_name,obj_name):
# generate s3select statement using generated randome expression
# statement return an arithmetical result for the generated expression.
# the same expression is evaluated by python-engine, result should be close enough(Epsilon)
e = random_expr( 4 )
try:
eval( e )
except ZeroDivisionError:
return
if eval( e ) == 0:
return
res = remove_xml_tags_from_result( run_s3select(bucket_name,obj_name,"select " + e + " from stdin;",) ).replace(",","")
# accuracy level
epsilon = float(0.000001)
# both results should be close (epsilon)
assert (1 - (float(res.split("\n")[1]) / eval( e )) ) < epsilon
@attr('s3select')
def test_generate_where_clause():
# create small csv file for testing the random expressions
single_line_csv = create_random_csv_object(1,1)
bucket_name = "test"
obj_name = "single_line_csv.csv"
upload_csv_object(bucket_name,obj_name,single_line_csv)
for _ in range(100):
generate_s3select_where_clause(bucket_name,obj_name)
@attr('s3select')
def test_generate_projection():
# create small csv file for testing the random expressions
single_line_csv = create_random_csv_object(1,1)
bucket_name = "test"
obj_name = "single_line_csv.csv"
upload_csv_object(bucket_name,obj_name,single_line_csv)
for _ in range(100):
generate_s3select_expression_projection(bucket_name,obj_name)
def create_csv_object_for_datetime(rows,columns):
result = ""
for _ in range(rows):
row = ""
for _ in range(columns):
row = row + "{}{:02d}{:02d}-{:02d}{:02d}{:02d},".format(random.randint(0,100)+1900,random.randint(1,12),random.randint(1,28),random.randint(0,23),random.randint(0,59),random.randint(0,59),)
result += row + "\n"
return result
def create_random_csv_object(rows,columns,col_delim=",",record_delim="\n",csv_schema=""):
result = ""
if len(csv_schema)>0 :
result = csv_schema + record_delim
for _ in range(rows):
row = ""
for _ in range(columns):
row = row + "{}{}".format(random.randint(0,1000),col_delim)
result += row + record_delim
return result
def upload_csv_object(bucket_name,new_key,obj):
client = get_client()
client.create_bucket(Bucket=bucket_name)
client.put_object(Bucket=bucket_name, Key=new_key, Body=obj)
def run_s3select(bucket,key,query,column_delim=",",row_delim="\n",quot_char='"',esc_char='\\',csv_header_info="NONE"):
s3 = get_client()
r = s3.select_object_content(
Bucket=bucket,
Key=key,
ExpressionType='SQL',
InputSerialization = {"CSV": {"RecordDelimiter" : row_delim, "FieldDelimiter" : column_delim,"QuoteEscapeCharacter": esc_char, "QuoteCharacter": quot_char, "FileHeaderInfo": csv_header_info}, "CompressionType": "NONE"},
OutputSerialization = {"CSV": {}},
Expression=query,)
result = ""
for event in r['Payload']:
if 'Records' in event:
records = event['Records']['Payload'].decode('utf-8')
result += records
return result
def remove_xml_tags_from_result(obj):
result = ""
for rec in obj.split("\n"):
if(rec.find("Payload")>0 or rec.find("Records")>0):
continue
result += rec + "\n" # remove by split
return result
def create_list_of_int(column_pos,obj,field_split=",",row_split="\n"):
list_of_int = []
for rec in obj.split(row_split):
col_num = 1
if ( len(rec) == 0):
continue
for col in rec.split(field_split):
if (col_num == column_pos):
list_of_int.append(int(col))
col_num+=1
return list_of_int
@attr('s3select')
def test_count_operation():
csv_obj_name = "csv_star_oper"
bucket_name = "test"
num_of_rows = 10
obj_to_load = create_random_csv_object(num_of_rows,10)
upload_csv_object(bucket_name,csv_obj_name,obj_to_load)
res = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select count(0) from stdin;") ).replace(",","")
nose.tools.assert_equal( num_of_rows, int( res ))
@attr('s3select')
def test_column_sum_min_max():
csv_obj = create_random_csv_object(10000,10)
csv_obj_name = "csv_10000x10"
bucket_name = "test"
upload_csv_object(bucket_name,csv_obj_name,csv_obj)
csv_obj_name = "csv_10000x10"
bucket_name_2 = "testbuck2"
upload_csv_object(bucket_name_2,csv_obj_name,csv_obj)
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select min(int(_1)) from stdin;") ).replace(",","")
list_int = create_list_of_int( 1 , csv_obj )
res_target = min( list_int )
nose.tools.assert_equal( int(res_s3select), int(res_target))
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select min(int(_4)) from stdin;") ).replace(",","")
list_int = create_list_of_int( 4 , csv_obj )
res_target = min( list_int )
nose.tools.assert_equal( int(res_s3select), int(res_target))
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select max(int(_4)) from stdin;") ).replace(",","")
list_int = create_list_of_int( 4 , csv_obj )
res_target = max( list_int )
nose.tools.assert_equal( int(res_s3select), int(res_target))
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select max(int(_7)) from stdin;") ).replace(",","")
list_int = create_list_of_int( 7 , csv_obj )
res_target = max( list_int )
nose.tools.assert_equal( int(res_s3select), int(res_target))
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select sum(int(_4)) from stdin;") ).replace(",","")
list_int = create_list_of_int( 4 , csv_obj )
res_target = sum( list_int )
nose.tools.assert_equal( int(res_s3select), int(res_target))
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select sum(int(_7)) from stdin;") ).replace(",","")
list_int = create_list_of_int( 7 , csv_obj )
res_target = sum( list_int )
nose.tools.assert_equal( int(res_s3select) , int(res_target) )
# the following queries, validates on *random* input an *accurate* relation between condition result,sum operation and count operation.
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name_2,csv_obj_name,"select count(0),sum(int(_1)),sum(int(_2)) from stdin where (int(_1)-int(_2)) == 2;" ) )
count,sum1,sum2,d = res_s3select.split(",")
nose.tools.assert_equal( int(count)*2 , int(sum1)-int(sum2 ) )
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select count(0),sum(int(_1)),sum(int(_2)) from stdin where (int(_1)-int(_2)) == 4;" ) )
count,sum1,sum2,d = res_s3select.split(",")
nose.tools.assert_equal( int(count)*4 , int(sum1)-int(sum2) )
@attr('s3select')
def test_complex_expressions():
# purpose of test: engine is process correctly several projections containing aggregation-functions
csv_obj = create_random_csv_object(10000,10)
csv_obj_name = "csv_100000x10"
bucket_name = "test"
upload_csv_object(bucket_name,csv_obj_name,csv_obj)
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select min(int(_1)),max(int(_2)),min(int(_3))+1 from stdin;")).replace("\n","")
min_1 = min ( create_list_of_int( 1 , csv_obj ) )
max_2 = max ( create_list_of_int( 2 , csv_obj ) )
min_3 = min ( create_list_of_int( 3 , csv_obj ) ) + 1
__res = "{},{},{},".format(min_1,max_2,min_3)
# assert is according to radom-csv function
nose.tools.assert_equal( res_s3select, __res )
# purpose of test that all where conditions create the same group of values, thus same result
res_s3select_substr = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select min(int(_2)),max(int(_2)) from stdin where substr(_2,1,1) == "1"')).replace("\n","")
res_s3select_between_numbers = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select min(int(_2)),max(int(_2)) from stdin where int(_2)>=100 and int(_2)<200')).replace("\n","")
res_s3select_eq_modolu = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select min(int(_2)),max(int(_2)) from stdin where int(_2)/100 == 1 or int(_2)/10 == 1 or int(_2) == 1')).replace("\n","")
nose.tools.assert_equal( res_s3select_substr, res_s3select_between_numbers)
nose.tools.assert_equal( res_s3select_between_numbers, res_s3select_eq_modolu)
@attr('s3select')
def test_alias():
# purpose: test is comparing result of exactly the same queries , one with alias the other without.
# this test is setting alias on 3 projections, the third projection is using other projection alias, also the where clause is using aliases
# the test validate that where-clause and projections are executing aliases correctly, bare in mind that each alias has its own cache,
# and that cache need to be invalidate per new row.
csv_obj = create_random_csv_object(10000,10)
csv_obj_name = "csv_10000x10"
bucket_name = "test"
upload_csv_object(bucket_name,csv_obj_name,csv_obj)
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select int(_1) as a1, int(_2) as a2 , (a1+a2) as a3 from stdin where a3>100 and a3<300;") ).replace(",","")
res_s3select_no_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select int(_1),int(_2),int(_1)+int(_2) from stdin where (int(_1)+int(_2))>100 and (int(_1)+int(_2))<300;") ).replace(",","")
nose.tools.assert_equal( res_s3select_alias, res_s3select_no_alias)
@attr('s3select')
def test_alias_cyclic_refernce():
number_of_rows = 10000
# purpose of test is to validate the s3select-engine is able to detect a cyclic reference to alias.
csv_obj = create_random_csv_object(number_of_rows,10)
csv_obj_name = "csv_10000x10"
bucket_name = "test"
upload_csv_object(bucket_name,csv_obj_name,csv_obj)
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select int(_1) as a1,int(_2) as a2, a1+a4 as a3, a5+a1 as a4, int(_3)+a3 as a5 from stdin;") )
find_res = res_s3select_alias.find("number of calls exceed maximum size, probably a cyclic reference to alias")
assert int(find_res) >= 0
@attr('s3select')
def test_datetime():
# purpose of test is to validate date-time functionality is correct,
# by creating same groups with different functions (nested-calls) ,which later produce the same result
csv_obj = create_csv_object_for_datetime(10000,1)
csv_obj_name = "csv_datetime_10000x10"
bucket_name = "test"
upload_csv_object(bucket_name,csv_obj_name,csv_obj)
res_s3select_date_time = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select count(0) from stdin where extract("year",timestamp(_1)) > 1950 and extract("year",timestamp(_1)) < 1960;') )
res_s3select_substr = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select count(0) from stdin where int(substr(_1,1,4))>1950 and int(substr(_1,1,4))<1960;') )
nose.tools.assert_equal( res_s3select_date_time, res_s3select_substr)
res_s3select_date_time = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select count(0) from stdin where datediff("month",timestamp(_1),dateadd("month",2,timestamp(_1)) ) == 2;') )
res_s3select_count = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select count(0) from stdin;') )
nose.tools.assert_equal( res_s3select_date_time, res_s3select_count)
res_s3select_date_time = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select count(0) from stdin where datediff("year",timestamp(_1),dateadd("day", 366 ,timestamp(_1))) == 1 ;') )
nose.tools.assert_equal( res_s3select_date_time, res_s3select_count)
# validate that utcnow is integrate correctly with other date-time functions
res_s3select_date_time_utcnow = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select count(0) from stdin where datediff("hours",utcnow(),dateadd("day",1,utcnow())) == 24 ;') )
nose.tools.assert_equal( res_s3select_date_time_utcnow, res_s3select_count)
@attr('s3select')
def test_csv_parser():
# purpuse: test default csv values(, \n " \ ), return value may contain meta-char
# NOTE: should note that default meta-char for s3select are also for python, thus for one example double \ is mandatory
csv_obj = ',first,,,second,third="c31,c32,c33",forth="1,2,3,4",fifth="my_string=\\"any_value\\" , my_other_string=\\"aaaa,bbb\\" ",' + "\n"
csv_obj_name = "csv_one_line"
bucket_name = "test"
upload_csv_object(bucket_name,csv_obj_name,csv_obj)
# return value contain comma{,}
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _6 from stdin;") ).replace("\n","")
nose.tools.assert_equal( res_s3select_alias, 'third="c31,c32,c33",')
# return value contain comma{,}
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _7 from stdin;") ).replace("\n","")
nose.tools.assert_equal( res_s3select_alias, 'forth="1,2,3,4",')
# return value contain comma{,}{"}, escape-rule{\} by-pass quote{"} , the escape{\} is removed.
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _8 from stdin;") ).replace("\n","")
nose.tools.assert_equal( res_s3select_alias, 'fifth="my_string="any_value" , my_other_string="aaaa,bbb" ",')
# return NULL as first token
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _1 from stdin;") ).replace("\n","")
nose.tools.assert_equal( res_s3select_alias, ',')
# return NULL in the middle of line
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _3 from stdin;") ).replace("\n","")
nose.tools.assert_equal( res_s3select_alias, ',')
# return NULL in the middle of line (successive)
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _4 from stdin;") ).replace("\n","")
nose.tools.assert_equal( res_s3select_alias, ',')
# return NULL at the end line
res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _9 from stdin;") ).replace("\n","")
nose.tools.assert_equal( res_s3select_alias, ',')
@attr('s3select')
def test_csv_definition():
number_of_rows = 10000
#create object with pipe-sign as field separator and tab as row delimiter.
csv_obj = create_random_csv_object(number_of_rows,10,"|","\t")
csv_obj_name = "csv_pipeSign_tab_eol"
bucket_name = "test"
upload_csv_object(bucket_name,csv_obj_name,csv_obj)
# purpose of tests is to parse correctly input with different csv defintions
res = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select count(0) from stdin;","|","\t") ).replace(",","")
nose.tools.assert_equal( number_of_rows, int(res))
# assert is according to radom-csv function
# purpose of test is validate that tokens are processed correctly
res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select min(int(_1)),max(int(_2)),min(int(_3))+1 from stdin;","|","\t") ).replace("\n","")
min_1 = min ( create_list_of_int( 1 , csv_obj , "|","\t") )
max_2 = max ( create_list_of_int( 2 , csv_obj , "|","\t") )
min_3 = min ( create_list_of_int( 3 , csv_obj , "|","\t") ) + 1
__res = "{},{},{},".format(min_1,max_2,min_3)
nose.tools.assert_equal( res_s3select, __res )
@attr('s3select')
def test_schema_definition():
number_of_rows = 10000
# purpose of test is to validate functionality using csv header info
csv_obj = create_random_csv_object(number_of_rows,10,csv_schema="c1,c2,c3,c4,c5,c6,c7,c8,c9,c10")
csv_obj_name = "csv_with_header_info"
bucket_name = "test"
upload_csv_object(bucket_name,csv_obj_name,csv_obj)
# ignoring the schema on first line and retrieve using generic column number
res_ignore = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _1,_3 from stdin;",csv_header_info="IGNORE") ).replace("\n","")
# using the scheme on first line, query is using the attach schema
res_use = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select c1,c3 from stdin;",csv_header_info="USE") ).replace("\n","")
# result of both queries should be the same
nose.tools.assert_equal( res_ignore, res_use)
# using column-name not exist in schema
res_multiple_defintion = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select c1,c10,int(c11) from stdin;",csv_header_info="USE") ).replace("\n","")
assert res_multiple_defintion.find("alias {c11} or column not exist in schema") > 0
# alias-name is identical to column-name
res_multiple_defintion = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select int(c1)+int(c2) as c4,c4 from stdin;",csv_header_info="USE") ).replace("\n","")
assert res_multiple_defintion.find("multiple definition of column {c4} as schema-column and alias") > 0
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