SparkSQL在機器學習場景中應用
第四范式已經在很多行業落地了上萬個AI應用，比如在金融行業的反欺詐，媒體行業的新聞推薦，能源行業管道檢測，而SparkSQL在這些AI應用中快速實現特征變換發揮著重要的作用
SparkSQL在特征變換主要有一下幾類
- 多表場景，用于表之間拼接操作，比如交易信息表去拼接賬戶表
- 使用udf進行簡單的特征變換，比如對時間戳進行hour函數處理
- 使用時間窗口和udaf進行時序類特征處理，比如計算一個人最近1天的消費金額總和
SparkSQL到目前為止，解決很好的解決離線模型訓練特征變換問題，但是隨著AI應用的發展，大家對模型的期望不再只是得出離線調研效果，而是在真實的業務場景發揮出價值，而真實的業務場景是模型應用場景，它需要高性能，需要實時推理，這時候我們就會遇到以下問題
- 多表數據離線到在線怎么映射，即批量訓練過程中輸入很多表，到在線環境這些表該以什么形式存在，這點也會影響整個系統架構，做得好能夠提升效率，做得不好就會大大增加模型產生業務價值的成本
- SQL轉換成實時執行成本高，因為在線推理需要高性能，而數據科學家可能做出成千上萬個特征，每個特征都人肉轉換，會大大增加的工程成本
- 離線特征和在線特征保持一致困難，手動轉換就會導致一致性能，而且往往很難一致
- 離線效果很棒但是在線效果無法滿足業務需求
在具體的反欺詐場景，模型應用要求tp99 20ms去檢測一筆交易是否是欺詐，所以對模型應用性能要求非常高
第四范式特征工程數據庫是如何解決這些問題
通過特征工程數據庫讓SparkSQL的能力得到了補充
- 以數據庫的形式，解決了離線表到在線的映射問題，我們對前面給出的答案就是離線表是怎么分布的，在線也就怎么分布
- 通過同一套代碼去執行離線和在線特征轉換，讓在線模型效果得到了保證
- 數據科學家與業務開發團隊的合作以sql為傳遞介質，而不再是手工去轉換代碼，大大提升模型迭代效率
- 通過llvm加速的sql，相比scala實現的spark2.x和3.x在時序復雜特征場景能夠加速2～3倍，在線通過in-memory的存儲，能夠保證sql能夠在非常低延遲返回結果
快速將spark sql 模型變成實時服務demo
demo的模型訓練場景為預測一次打車行程到結束所需要的時間，這里我們將使用fedb ，pyspark，lightgbm等工具最終搭建一個http 模型推理服務，這也會是spark在機器學習場景的實踐
整個demo200多行代碼，制作時間不超過半個小時
- train_sql.py 特征計算與訓練, 80行代碼
- predict_server.py 模型推理http服務, 129行代碼
場景數據和特征介紹
整個訓練數據如下樣子
樣例數據
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 `
場景特征變換sql腳本
特征變換
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) `
我們選擇了vendor_id 和 passenger_count 兩個緯度做時序特征
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' )執行模型訓練過程，最終產生model.txt
模型推理過程
導入數據代碼
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為訓練數據csv格式版本
模型推理邏輯
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 ]))``
最終執行效果
# 發送推理請求 ,會看到如下輸出
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 `
運行demo請到 https://github.com/4paradigm/SparkSQLWithFeDB
