// To mute tests follow example in file: example.csv-spec // // Tests testing field alias (introduced in ES 6.4) // // filtering filterEquals SELECT extra.info.gender gender FROM "test_emp_copy" WHERE gender = 'M' LIMIT 5; gender --------------- M M M M M ; filterNotEquals SELECT extra.info.gender gender FROM "test_emp_copy" WHERE gender <> 'M' ORDER BY gender LIMIT 5; gender --------------- F F F F F ; aggWithNullFilter SELECT COUNT(*) count FROM test_emp_copy WHERE extra.info.gender IS NULL; count:l --------------- 10 ; functionOverAlias SELECT BIT_LENGTH(extra.info.gender) bit FROM test_emp_copy ORDER BY extra.info.gender LIMIT 1; bit --------------- 8 ; singlePercentileWithoutComma SELECT extra.info.gender AS gender, PERCENTILE(emp_no, 97) p1 FROM test_emp_copy GROUP BY extra.info.gender; gender:s | p1:d null |10019.0 F |10099.51 M |10095.789999999999 ; singlePercentileWithComma SELECT extra.info.gender AS gender, PERCENTILE(emp_no, 97.76) p1 FROM test_emp_copy GROUP BY extra.info.gender; gender:s | p1:d null |10019.0 F |10099.7608 M |10096.2232 ; multiplePercentilesOneWithCommaOneWithout SELECT extra.info.gender AS gender, PERCENTILE(emp_no, 92.45) p1, PERCENTILE(emp_no, 91) p2 FROM test_emp_copy GROUP BY extra.info.gender; gender:s | p1:d | p2:d null |10018.745 |10018.599999999999 F |10096.336 |10094.48 M |10091.393 |10090.37 ; multiplePercentilesWithoutComma SELECT extra.info.gender AS gender, PERCENTILE(emp_no, 91) p1, PERCENTILE(emp_no, 89) p2 FROM test_emp_copy GROUP BY extra.info.gender; gender:s | p1:d | p2:d null |10018.599999999999 |10018.4 F |10094.48 |10093.74 M |10090.37 |10085.84 ; multiplePercentilesWithComma SELECT extra.info.gender AS gender, PERCENTILE(emp_no, 85.7) p1, PERCENTILE(emp_no, 94.3) p2 FROM test_emp_copy GROUP BY extra.info.gender; gender:s | p1:d | p2:d null |10018.070000000002 |10018.929999999998 F |10090.272 |10098.619 M |10084.349 |10093.502 ; percentileRank SELECT extra.info.gender AS gender, PERCENTILE_RANK(emp_no, 10025) rank FROM test_emp_copy GROUP BY extra.info.gender; gender:s | rank:d null |100.0 F |17.424242424242426 M |15.350877192982457 ; multiplePercentileRanks SELECT extra.info.gender AS gender, PERCENTILE_RANK(emp_no, 10030.0) rank1, PERCENTILE_RANK(emp_no, 10025) rank2 FROM test_emp_copy GROUP BY extra.info.gender; gender:s | rank1:d | rank2:d null |100.0 |100.0 F |21.445221445221442 |17.424242424242426 M |21.929824561403507 |15.350877192982457 ; multiplePercentilesAndPercentileRank SELECT extra.info.gender AS gender, PERCENTILE(emp_no, 97.76) p1, PERCENTILE(emp_no, 93.3) p2, PERCENTILE_RANK(emp_no, 10025) rank FROM test_emp_copy GROUP BY extra.info.gender; gender:s | p1:d | p2:d | rank:d null |10019.0 |10018.83 |100.0 F |10099.7608 |10098.289 |17.424242424242426 M |10096.2232 |10092.362 |15.350877192982457 ; kurtosisAndSkewnessGroup SELECT extra.info.gender AS gender, KURTOSIS(salary) k, SKEWNESS(salary) s FROM test_emp_copy GROUP BY extra.info.gender; gender:s | k:d | s:d null |2.2215791166941923 |-0.03373126000214023 F |1.7873117044424276 |0.05504995122217512 M |2.280646181070106 |0.44302407229580243 ;