import nose import random from nose.plugins.attrib import attr import uuid from nose.tools import eq_ as eq 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 get_random_string(): return uuid.uuid4().hex[:6].upper() 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 = get_random_string() #"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 = get_random_string() #"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) # validate uploaded object c2 = get_client() response = c2.get_object(Bucket=bucket_name, Key=new_key) eq(response['Body'].read().decode('utf-8'), obj, 's3select error[ downloaded object not equal to uploaded objecy') 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 = get_random_string() bucket_name = "test" num_of_rows = 1234 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 = get_random_string() bucket_name = "test" upload_csv_object(bucket_name,csv_obj_name,csv_obj) csv_obj_name_2 = get_random_string() bucket_name_2 = "testbuck2" upload_csv_object(bucket_name_2,csv_obj_name_2,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_2,"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 = get_random_string() 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 = get_random_string() 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 = get_random_string() 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 = get_random_string() 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 = get_random_string() 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 = get_random_string() 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 = get_random_string() 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 @attr('s3select') def test_version(): return number_of_rows = 1 # purpose of test is to validate functionality using csv header info csv_obj = create_random_csv_object(number_of_rows,10) csv_obj_name = get_random_string() bucket_name = "test" upload_csv_object(bucket_name,csv_obj_name,csv_obj) res_version = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select version() from stdin;") ).replace("\n","") nose.tools.assert_equal( res_version, "41.a," )