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Merge pull request #342 from ceph/s3select_first_tests_framework
commit first tests for s3select and initial framework
This commit is contained in:
Matt Benjamin
GitHub
commit
0985cc11d7
1 changed files with 425 additions and 0 deletions
425
s3tests_boto3/functional/test_s3select.py
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s3tests_boto3/functional/test_s3select.py
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import nose
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import random
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from . import (
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get_client
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)
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region_name = ''
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# recurssion function for generating arithmetical expression
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def random_expr(depth):
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# depth is the complexity of expression
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if depth==1 :
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return str(int(random.random() * 100) + 1)+".0"
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return '(' + random_expr(depth-1) + random.choice(['+','-','*','/']) + random_expr(depth-1) + ')'
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def generate_s3select_where_clause(bucket_name,obj_name):
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a=random_expr(4)
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b=random_expr(4)
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s=random.choice([ '<','>','==','<=','>=','!=' ])
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try:
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eval( a )
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eval( b )
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except ZeroDivisionError:
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return
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# generate s3select statement using generated randome expression
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# upon count(0)>0 it means true for the where clause expression
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# the python-engine {eval( conditional expression )} should return same boolean result.
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s3select_stmt = "select count(0) from stdin where " + a + s + b + ";"
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res = remove_xml_tags_from_result( run_s3select(bucket_name,obj_name,s3select_stmt) ).replace(",","")
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nose.tools.assert_equal(int(res)>0 , eval( a + s + b ))
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def generate_s3select_expression_projection(bucket_name,obj_name):
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# generate s3select statement using generated randome expression
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# statement return an arithmetical result for the generated expression.
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# the same expression is evaluated by python-engine, result should be close enough(Epsilon)
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e = random_expr( 4 )
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try:
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eval( e )
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except ZeroDivisionError:
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return
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if eval( e ) == 0:
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return
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res = remove_xml_tags_from_result( run_s3select(bucket_name,obj_name,"select " + e + " from stdin;",) ).replace(",","")
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# accuracy level
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epsilon = float(0.000001)
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# both results should be close (epsilon)
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assert (1 - (float(res.split("\n")[1]) / eval( e )) ) < epsilon
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def test_generate_where_clause():
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# create small csv file for testing the random expressions
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single_line_csv = create_random_csv_object(1,1)
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bucket_name = "test"
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obj_name = "single_line_csv.csv"
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upload_csv_object(bucket_name,obj_name,single_line_csv)
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for _ in range(100):
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generate_s3select_where_clause(bucket_name,obj_name)
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def test_generate_projection():
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# create small csv file for testing the random expressions
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single_line_csv = create_random_csv_object(1,1)
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bucket_name = "test"
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obj_name = "single_line_csv.csv"
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upload_csv_object(bucket_name,obj_name,single_line_csv)
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for _ in range(100):
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generate_s3select_expression_projection(bucket_name,obj_name)
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def create_csv_object_for_datetime(rows,columns):
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result = ""
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for _ in range(rows):
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row = ""
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for _ in range(columns):
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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),)
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result += row + "\n"
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return result
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def create_random_csv_object(rows,columns,col_delim=",",record_delim="\n",csv_schema=""):
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result = ""
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if len(csv_schema)>0 :
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result = csv_schema + record_delim
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for _ in range(rows):
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row = ""
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for _ in range(columns):
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row = row + "{}{}".format(random.randint(0,1000),col_delim)
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result += row + record_delim
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return result
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def upload_csv_object(bucket_name,new_key,obj):
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client = get_client()
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client.create_bucket(Bucket=bucket_name)
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client.put_object(Bucket=bucket_name, Key=new_key, Body=obj)
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def run_s3select(bucket,key,query,column_delim=",",row_delim="\n",quot_char='"',esc_char='\\',csv_header_info="NONE"):
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s3 = get_client()
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r = s3.select_object_content(
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Bucket=bucket,
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Key=key,
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ExpressionType='SQL',
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InputSerialization = {"CSV": {"RecordDelimiter" : row_delim, "FieldDelimiter" : column_delim,"QuoteEscapeCharacter": esc_char, "QuoteCharacter": quot_char, "FileHeaderInfo": csv_header_info}, "CompressionType": "NONE"},
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OutputSerialization = {"CSV": {}},
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Expression=query,)
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result = ""
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for event in r['Payload']:
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if 'Records' in event:
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records = event['Records']['Payload'].decode('utf-8')
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result += records
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return result
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def remove_xml_tags_from_result(obj):
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result = ""
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for rec in obj.split("\n"):
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if(rec.find("Payload")>0 or rec.find("Records")>0):
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continue
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result += rec + "\n" # remove by split
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return result
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def create_list_of_int(column_pos,obj,field_split=",",row_split="\n"):
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list_of_int = []
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for rec in obj.split(row_split):
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col_num = 1
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if ( len(rec) == 0):
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continue
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for col in rec.split(field_split):
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if (col_num == column_pos):
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list_of_int.append(int(col))
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col_num+=1
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return list_of_int
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def test_count_operation():
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csv_obj_name = "csv_star_oper"
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bucket_name = "test"
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num_of_rows = 10
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obj_to_load = create_random_csv_object(num_of_rows,10)
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upload_csv_object(bucket_name,csv_obj_name,obj_to_load)
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res = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select count(0) from stdin;") ).replace(",","")
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nose.tools.assert_equal( num_of_rows, int( res ))
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def test_column_sum_min_max():
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csv_obj = create_random_csv_object(10000,10)
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csv_obj_name = "csv_10000x10"
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bucket_name = "test"
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upload_csv_object(bucket_name,csv_obj_name,csv_obj)
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csv_obj_name = "csv_10000x10"
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bucket_name_2 = "testbuck2"
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upload_csv_object(bucket_name_2,csv_obj_name,csv_obj)
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res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select min(int(_1)) from stdin;") ).replace(",","")
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list_int = create_list_of_int( 1 , csv_obj )
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res_target = min( list_int )
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nose.tools.assert_equal( int(res_s3select), int(res_target))
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res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select min(int(_4)) from stdin;") ).replace(",","")
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list_int = create_list_of_int( 4 , csv_obj )
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res_target = min( list_int )
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nose.tools.assert_equal( int(res_s3select), int(res_target))
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res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select max(int(_4)) from stdin;") ).replace(",","")
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list_int = create_list_of_int( 4 , csv_obj )
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res_target = max( list_int )
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nose.tools.assert_equal( int(res_s3select), int(res_target))
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res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select max(int(_7)) from stdin;") ).replace(",","")
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list_int = create_list_of_int( 7 , csv_obj )
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res_target = max( list_int )
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nose.tools.assert_equal( int(res_s3select), int(res_target))
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res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select sum(int(_4)) from stdin;") ).replace(",","")
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list_int = create_list_of_int( 4 , csv_obj )
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res_target = sum( list_int )
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nose.tools.assert_equal( int(res_s3select), int(res_target))
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res_s3select = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select sum(int(_7)) from stdin;") ).replace(",","")
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list_int = create_list_of_int( 7 , csv_obj )
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res_target = sum( list_int )
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nose.tools.assert_equal( int(res_s3select) , int(res_target) )
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# the following queries, validates on *random* input an *accurate* relation between condition result,sum operation and count operation.
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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;" ) )
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count,sum1,sum2,d = res_s3select.split(",")
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nose.tools.assert_equal( int(count)*2 , int(sum1)-int(sum2 ) )
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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;" ) )
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count,sum1,sum2,d = res_s3select.split(",")
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nose.tools.assert_equal( int(count)*4 , int(sum1)-int(sum2) )
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def test_complex_expressions():
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# purpose of test: engine is process correctly several projections containing aggregation-functions
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csv_obj = create_random_csv_object(10000,10)
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csv_obj_name = "csv_100000x10"
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bucket_name = "test"
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upload_csv_object(bucket_name,csv_obj_name,csv_obj)
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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","")
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min_1 = min ( create_list_of_int( 1 , csv_obj ) )
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max_2 = max ( create_list_of_int( 2 , csv_obj ) )
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min_3 = min ( create_list_of_int( 3 , csv_obj ) ) + 1
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__res = "{},{},{},".format(min_1,max_2,min_3)
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# assert is according to radom-csv function
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nose.tools.assert_equal( res_s3select, __res )
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# purpose of test that all where conditions create the same group of values, thus same result
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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","")
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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","")
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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","")
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nose.tools.assert_equal( res_s3select_substr, res_s3select_between_numbers)
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nose.tools.assert_equal( res_s3select_between_numbers, res_s3select_eq_modolu)
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def test_alias():
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# purpose: test is comparing result of exactly the same queries , one with alias the other without.
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# this test is setting alias on 3 projections, the third projection is using other projection alias, also the where clause is using aliases
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# the test validate that where-clause and projections are executing aliases correctly, bare in mind that each alias has its own cache,
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# and that cache need to be invalidate per new row.
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csv_obj = create_random_csv_object(10000,10)
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csv_obj_name = "csv_10000x10"
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bucket_name = "test"
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upload_csv_object(bucket_name,csv_obj_name,csv_obj)
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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(",","")
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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(",","")
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nose.tools.assert_equal( res_s3select_alias, res_s3select_no_alias)
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def test_alias_cyclic_refernce():
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number_of_rows = 10000
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# purpose of test is to validate the s3select-engine is able to detect a cyclic reference to alias.
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csv_obj = create_random_csv_object(number_of_rows,10)
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csv_obj_name = "csv_10000x10"
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bucket_name = "test"
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upload_csv_object(bucket_name,csv_obj_name,csv_obj)
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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;") )
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find_res = res_s3select_alias.find("number of calls exceed maximum size, probably a cyclic reference to alias")
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assert int(find_res) >= 0
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def test_datetime():
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# purpose of test is to validate date-time functionality is correct,
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# by creating same groups with different functions (nested-calls) ,which later produce the same result
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csv_obj = create_csv_object_for_datetime(10000,1)
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csv_obj_name = "csv_datetime_10000x10"
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bucket_name = "test"
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upload_csv_object(bucket_name,csv_obj_name,csv_obj)
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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;') )
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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;') )
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nose.tools.assert_equal( res_s3select_date_time, res_s3select_substr)
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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;') )
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res_s3select_count = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,'select count(0) from stdin;') )
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nose.tools.assert_equal( res_s3select_date_time, res_s3select_count)
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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 ;') )
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nose.tools.assert_equal( res_s3select_date_time, res_s3select_count)
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# validate that utcnow is integrate correctly with other date-time functions
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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 ;') )
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nose.tools.assert_equal( res_s3select_date_time_utcnow, res_s3select_count)
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def test_csv_parser():
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# purpuse: test default csv values(, \n " \ ), return value may contain meta-char
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# NOTE: should note that default meta-char for s3select are also for python, thus for one example double \ is mandatory
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csv_obj = ',first,,,second,third="c31,c32,c33",forth="1,2,3,4",fifth="my_string=\\"any_value\\" , my_other_string=\\"aaaa,bbb\\" ",' + "\n"
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csv_obj_name = "csv_one_line"
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bucket_name = "test"
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upload_csv_object(bucket_name,csv_obj_name,csv_obj)
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# return value contain comma{,}
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res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _6 from stdin;") ).replace("\n","")
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nose.tools.assert_equal( res_s3select_alias, 'third="c31,c32,c33",')
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# return value contain comma{,}
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res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _7 from stdin;") ).replace("\n","")
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nose.tools.assert_equal( res_s3select_alias, 'forth="1,2,3,4",')
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# return value contain comma{,}{"}, escape-rule{\} by-pass quote{"} , the escape{\} is removed.
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res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _8 from stdin;") ).replace("\n","")
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nose.tools.assert_equal( res_s3select_alias, 'fifth="my_string="any_value" , my_other_string="aaaa,bbb" ",')
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# return NULL as first token
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res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _1 from stdin;") ).replace("\n","")
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nose.tools.assert_equal( res_s3select_alias, ',')
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# return NULL in the middle of line
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res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _3 from stdin;") ).replace("\n","")
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nose.tools.assert_equal( res_s3select_alias, ',')
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# return NULL in the middle of line (successive)
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res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _4 from stdin;") ).replace("\n","")
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nose.tools.assert_equal( res_s3select_alias, ',')
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# return NULL at the end line
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res_s3select_alias = remove_xml_tags_from_result( run_s3select(bucket_name,csv_obj_name,"select _9 from stdin;") ).replace("\n","")
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nose.tools.assert_equal( res_s3select_alias, ',')
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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 )
|
||||
|
||||
|
||||
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
|
||||
Loading…
Reference in a new issue