SparkSQL在机器学习场景中应用

第四范式已经在很多行业落地了上万个AI应用，比如在金融行业的反欺诈，媒体行业的新闻推荐，能源行业管道检测，而SparkSQL在这些AI应用中快速实现特征变换发挥着重要的作用

SparkSQL在特征变换主要有一下几类

1. 多表场景，用于表之间拼接操作，比如交易信息表去拼接账户表
2. 使用udf进行简单的特征变换，比如对时间戳进行hour函数处理
3. 使用时间窗口和udaf进行时序类特征处理，比如计算一个人最近1天的消费金额总和

SparkSQL到目前为止，解决很好的解决离线模型训练特征变换问题，但是随着AI应用的发展，大家对模型的期望不再只是得出离线调研效果，而是在真实的业务场景发挥出价值，而真实的业务场景是模型应用场景，它需要高性能，需要实时推理，这时候我们就会遇到以下问题

1. 多表数据离线到在线怎么映射，即批量训练过程中输入很多表，到在线环境这些表该以什么形式存在，这点也会影响整个系统架构，做得好能够提升效率，做得不好就会大大增加模型产生业务价值的成本
2. SQL转换成实时执行成本高，因为在线推理需要高性能，而数据科学家可能做出成千上万个特征，每个特征都人肉转换，会大大增加的工程成本
3. 离线特征和在线特征保持一致困难，手动转换就会导致一致性能，而且往往很难一致
4. 离线效果很棒但是在线效果无法满足业务需求

在具体的反欺诈场景，模型应用要求tp99 20ms去检测一笔交易是否是欺诈，所以对模型应用性能要求非常高

第四范式特征工程数据库是如何解决这些问题

通过特征工程数据库让SparkSQL的能力得到了补充

1. 以数据库的形式，解决了离线表到在线的映射问题，我们对前面给出的答案就是离线表是怎么分布的，在线也就怎么分布
2. 通过同一套代码去执行离线和在线特征转换，让在线模型效果得到了保证
3. 数据科学家与业务开发团队的合作以sql为传递介质，而不再是手工去转换代码，大大提升模型迭代效率
4. 通过llvm加速的sql，相比scala实现的spark2.x和3.x在时序复杂特征场景能够加速2～3倍，在线通过in-memory的存储，能够保证sql能够在非常低延迟返回结果

快速将spark sql 模型变成实时服务demo

demo的模型训练场景为预测一次打车行程到结束所需要的时间，这里我们将使用fedb ，pyspark，lightgbm等工具最终搭建一个http 模型推理服务，这也会是spark在机器学习场景的实践

整个demo200多行代码，制作时间不超过半个小时

1. train_sql.py 特征计算与训练, 80行代码
2. predict_server.py 模型推理http服务, 129行代码

场景数据和特征介绍

整个训练数据如下样子

样例数据

id,vendor_id,pickup_datetime,dropoff_datetime,passenger_count,pickup_longitude,pickup_latitude,dropoff_longitude,dropoff_latitude,store_and_fwd_flag,trip_duration

id3097625,1,2016-01-22 16:01:00,2016-01-22 16:15:16,2,-73.97746276855469,40.7613525390625,-73.95573425292969,40.772396087646484,N,856

id3196697,1,2016-01-28 07:20:18,2016-01-28 07:40:16,1,-73.98524475097656,40.75959777832031,-73.99615478515625,40.72945785522461,N,1198

id0224515,2,2016-01-31 00:48:27,2016-01-31 00:53:30,1,-73.98342895507812,40.7500114440918,-73.97383880615234,40.74980163574219,N,303

id3370903,1,2016-01-14 11:46:43,2016-01-14 12:25:33,2,-74.00027465820312,40.74786376953125,-73.86485290527344,40.77039337158203,N,2330

id2763851,2,2016-02-20 13:21:00,2016-02-20 13:45:56,1,-73.95218658447266,40.772220611572266,-73.9920425415039,40.74932098388672,N,1496

id0904926,1,2016-02-20 19:17:44,2016-02-20 19:33:19,4,-73.97344207763672,40.75189971923828,-73.98480224609375,40.76243209838867,N,935

id2026293,1,2016-02-25 01:16:23,2016-02-25 01:31:27,1,-73.9871597290039,40.68777847290039,-73.9115219116211,40.68180847167969,N,904

id1349988,1,2016-01-28 20:16:05,2016-01-28 20:21:36,1,-74.0028076171875,40.7338752746582,-73.9968032836914,40.743770599365234,N,331

id3218692,2,2016-02-17 16:43:27,2016-02-17 16:54:41,5,-73.98147583007812,40.77408218383789,-73.97216796875,40.76400375366211,N,674 `

场景特征变换sql脚本

特征变换

select trip_duration, passenger_count,

sum `(pickup_latitude) over w as vendor_sum_pl,`

max `(pickup_latitude) over w as vendor_max_pl,`

min `(pickup_latitude) over w as vendor_min_pl,`

avg `(pickup_latitude) over w as vendor_avg_pl,`

sum `(pickup_latitude) over w2 as pc_sum_pl,`

max `(pickup_latitude) over w2 as pc_max_pl,`

min `(pickup_latitude) over w2 as pc_min_pl,`

avg `(pickup_latitude) over w2 as pc_avg_pl ,`

count `(vendor_id) over w2 as pc_cnt,`

count `(vendor_id) over w as vendor_cnt`

from {}

window w as (partition by vendor_id order by pickup_datetime ROWS_RANGE BETWEEN 1d PRECEDING AND CURRENT ROW),

w2 as (partition by passenger_count order by pickup_datetime ROWS_RANGE BETWEEN 1d PRECEDING AND CURRENT ROW) `

我们选择了vendor_id 和 passenger_count 两个纬度做时序特征

train_df = spark.sql(train_sql)

# specify your configurations as a dict

params = {

'boosting_type' `: 'gbdt' ,

'objective' `: 'regression' ,

'metric' `: { 'l2' , 'l1' },

'num_leaves' `: 31 ,

'learning_rate' `: 0.05 ,

'feature_fraction' `: 0.9 ,

'bagging_fraction' `: 0.8 ,

'bagging_freq' `: 5 ,

'verbose' `: 0`

}

print `( 'Starting training...' )`

gbm = lgb.train(params,

lgb_train,

num_boost_round `= 20 ,`

valid_sets `= lgb_eval,

early_stopping_rounds `= 5 )`

gbm.save_model( `'model.txt' )执行模型训练过程，最终产生model.txt

模型推理过程

导入数据代码

import

def insert_row(line):

row = line.split( `',' )

row[ `2 ]` `=` `'%dl' % int (datetime.datetime.strptime(row[ 2 ], '%Y-%m-%d %H:%M:%S' ).timestamp()` `*` `1000 )`

row[ `3 ]` `=` `'%dl' % int (datetime.datetime.strptime(row[ 3 ], '%Y-%m-%d %H:%M:%S' ).timestamp()` `*` `1000 )`

insert = "insert into t1 values('%s', %s, %s, %s, %s, %s, %s, %s, %s, '%s', %s);" `% tuple (row)

driver.executeInsert( `'db_test' , insert)

with open `( 'data/taxi_tour_table_train_simple.csv' , 'r' ) as fd:

idx = 0

for line in fd:

if idx = `= 0 :

idx = idx + 1

continue

insert_row(line.replace( `'n' , ''))

idx = idx + 1 `

注：train.csv为训练数据csv格式版本

模型推理逻辑

predict.py

def` `post( self ):

row = json.loads( `self .request.body)

ok, req = fedb_driver.getRequestBuilder( `'db_test' , sql)

if not ok or not req:

self `.write( "fail to get req" )`

return

input_schema = req.GetSchema()

if not input_schema:

self `.write( "no schema found" )`

return

str_length = 0

for i in range `(input_schema.GetColumnCnt()):`

if sql_router_sdk.DataTypeName(input_schema.GetColumnType(i)) = `= 'string' :

str_length = str_length + len `(row.get(input_schema.GetColumnName(i), ''))`

req.Init(str_length)

for i in range `(input_schema.GetColumnCnt()):`

tname = sql_router_sdk.DataTypeName(input_schema.GetColumnType(i))

if tname = `= 'string' :

req.AppendString(row.get(input_schema.GetColumnName(i), ''))

elif tname = `= 'int32' :

req.AppendInt32( `int (row.get(input_schema.GetColumnName(i),` `0 )))`

elif tname = `= 'double' :

req.AppendDouble( `float (row.get(input_schema.GetColumnName(i),` `0 )))`

elif tname = `= 'timestamp' :

req.AppendTimestamp( `int (row.get(input_schema.GetColumnName(i),` `0 )))`

else `:`

req.AppendNULL()

if not req.Build():

self `.write( "fail to build request" )`

return

ok, rs = fedb_driver.executeQuery( `'db_test' , sql, req)

if not ok:

self `.write( "fail to execute sql" )`

return

rs. `Next ()

ins = build_feature(rs)

self `.write( "----------------ins---------------n" )`

self `.write( str (ins) + "n" )

duration = bst.predict(ins)

self `.write( "---------------predict trip_duration -------------n" )`

self `.write( "%s s" % str (duration[ 0 ]))``

最终执行效果

# 发送推理请求 ,会看到如下输出

Python3 predict.py

----------------ins---------------

[[ 2. 40.774097 40.774097 40.774097 40.774097 40.774097 40.774097

40.774097 40.774097 1. 1. ]]

---------------predict trip_duration -------------

859.3298781277192 s `

运行demo请到 https://github.com/4paradigm/SparkSQLWithFeDB