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Do the same thing in Spark and Pandas
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| import time | |
| import pandas as pd | |
| store = pd.HDFStore('/Users/jiffyclub/synth/spark-demo/mtc_asim.h5') | |
| persons = store['persons'] | |
| households = store['households'] | |
| store.close() | |
| t1 = time.time() | |
| persons = persons.merge(households, left_on='household_id', right_index=True) | |
| t2 = time.time() | |
| print 'time to merge: {}'.format(t2 - t1) | |
| persons = persons.query('age >= 18 and income >= 10000') | |
| assert len(persons) > 0, 'no people left after query' | |
| t3 = time.time() | |
| print 'time to filter: {}'.format(t3 - t2) | |
| income_by_sex = persons.groupby('sex').income.mean() | |
| t4 = time.time() | |
| print 'time to groupby agg: {}'.format(t4 - t3) | |
| print 'total time: {}'.format(t4 - t1) | |
| print income_by_sex |
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| """ | |
| Ran from the command line with: | |
| time spark-submit --driver-memory 4g --master 'local[*]' spark_run.py 2> spark.log | |
| """ | |
| import time | |
| import pandas as pd | |
| from pyspark import SparkContext, SparkConf | |
| from pyspark.sql import SQLContext | |
| store = pd.HDFStore('/Users/jiffyclub/synth/spark-demo/mtc_asim.h5') | |
| persons = store['persons'].reset_index() | |
| households = store['households'].reset_index() | |
| store.close() | |
| spark_conf = ( | |
| SparkConf() | |
| .setAppName('SparkRunDemo') | |
| # .setMaster('local[*]') | |
| # .set('spark.driver.memory', '8g') | |
| .set('spark.executor.memory', '8g') | |
| .set('spark.python.worker.memory', '8g') | |
| .set('spark.storage.memoryFraction', 0.2) | |
| .set('spark.logConf', True)) | |
| print spark_conf.toDebugString() | |
| sc = SparkContext(conf=spark_conf) | |
| sql = SQLContext(sc) | |
| hh_spark = sql.createDataFrame(households) | |
| p_spark = sql.createDataFrame(persons) | |
| t1 = time.time() | |
| merged = hh_spark.join(p_spark, hh_spark.HHID == p_spark.household_id) | |
| t2 = time.time() | |
| print 'time to merge: {}'.format(t2 - t1) | |
| # filtered = merged.filter((merged.age <= 18) & (merged.income >= 100000)) | |
| filtered = merged.filter('age >= 18 and income >= 10000') | |
| t3 = time.time() | |
| print 'time to filter: {}'.format(t3 - t2) | |
| income_by_sex = filtered.groupby('sex').agg({'income': 'mean'}) | |
| t4 = time.time() | |
| print 'time to groupby agg: {}'.format(t4 - t3) | |
| print income_by_sex.collect() | |
| t5 = time.time() | |
| print 'time to collect: {}'.format(t5 - t4) | |
| print 'total time: {}'.format(t5 - t1) |
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