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Compiled Python UDFs for ImpalaUri Laserson20 May 2014

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Impala User-defined Functions (UDFs)

• Tuple => Scalar value• Substring• sin, cos, pow, …• Machine-learning models

• Supports Hive UDFs (Java)• Relatively unpleasurable• Slower

• Impala (native) UDFs• C++ interface designed for efficiency• Similar to Postgres UDFs• Runs any LLVM-compiled code

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LLVM compiler infrastructure

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LLVM: C++ example

bool StringEq(FunctionContext* context, const StringVal& arg1, const StringVal& arg2) { if (arg1.is_null != arg2.is_null) return false; if (arg1.is_null) return true; if (arg1.len != arg2.len) return false; return (arg1.ptr == arg2.ptr) || memcmp(arg1.ptr, arg2.ptr, arg1.len) == 0;}

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LLVM: IR output; ModuleID = '<stdin>'target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64-S128"target triple = "x86_64-apple-macosx10.7.0"

%"class.impala_udf::FunctionContext" = type { %"class.impala::FunctionContextImpl"* }%"class.impala::FunctionContextImpl" = type opaque%"struct.impala_udf::StringVal" = type { %"struct.impala_udf::AnyVal", i32, i8* }%"struct.impala_udf::AnyVal" = type { i8 }

; Function Attrs: nounwind readonly ssp uwtabledefine zeroext i1 @_Z8StringEqPN10impala_udf15FunctionContextERKNS_9StringValES4_(%"class.impala_udf::FunctionContext"* nocapture %context, %"struct.impala_udf::StringVal"* nocapture %arg1, %"struct.impala_udf::StringVal"* nocapture %arg2) #0 {entry: %is_null = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg1, i64 0, i32 0, i32 0 %0 = load i8* %is_null, align 1, !tbaa !0, !range !3 %is_null1 = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg2, i64 0, i32 0, i32 0 %1 = load i8* %is_null1, align 1, !tbaa !0, !range !3 %cmp = icmp eq i8 %0, %1 br i1 %cmp, label %if.end, label %return

if.end: ; preds = %entry %tobool = icmp eq i8 %0, 0 br i1 %tobool, label %if.end7, label %return

if.end7: ; preds = %if.end %len = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg1, i64 0, i32 1 %2 = load i32* %len, align 4, !tbaa !4 %len8 = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg2, i64 0, i32 1 %3 = load i32* %len8, align 4, !tbaa !4 %cmp9 = icmp eq i32 %2, %3 br i1 %cmp9, label %if.end11, label %return

if.end11: ; preds = %if.end7 %ptr = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg1, i64 0, i32 2 %4 = load i8** %ptr, align 8, !tbaa !5 %ptr12 = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg2, i64 0, i32 2 %5 = load i8** %ptr12, align 8, !tbaa !5 %cmp13 = icmp eq i8* %4, %5 br i1 %cmp13, label %return, label %lor.rhs

lor.rhs: ; preds = %if.end11 %conv17 = sext i32 %2 to i64 %call = tail call i32 @memcmp(i8* %4, i8* %5, i64 %conv17) %cmp18 = icmp eq i32 %call, 0 br label %return

return: ; preds = %lor.rhs, %if.end11, %if.end7, %if.end, %entry %retval.0 = phi i1 [ false, %entry ], [ true, %if.end ], [ false, %if.end7 ], [ true, %if.end11 ], [ %cmp18, %lor.rhs ] ret i1 %retval.0}

; Function Attrs: nounwind readonlydeclare i32 @memcmp(i8* nocapture, i8* nocapture, i64) #1

attributes #0 = { nounwind readonly ssp uwtable "less-precise-fpmad"="false" "no-frame-pointer-elim"="true" "no-frame-pointer-elim-non-leaf"="true" "no-infs-fp-math"="false" "no-nans-fp-math"="false" "unsafe-fp-math"="false" "use-soft-float"="false" }attributes #1 = { nounwind readonly "less-precise-fpmad"="false" "no-frame-pointer-elim"="true" "no-frame-pointer-elim-non-leaf"="true" "no-infs-fp-math"="false" "no-nans-fp-math"="false" "unsafe-fp-math"="false" "use-soft-float"="false" }

!0 = metadata !{metadata !"bool", metadata !1}!1 = metadata !{metadata !"omnipotent char", metadata !2}!2 = metadata !{metadata !"Simple C/C++ TBAA"}!3 = metadata !{i8 0, i8 2}!4 = metadata !{metadata !"int", metadata !1}!5 = metadata !{metadata !"any pointer", metadata !1}

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LLVM: IR output; ModuleID = '<stdin>'target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64-S128"target triple = "x86_64-apple-macosx10.7.0"

%"class.impala_udf::FunctionContext" = type { %"class.impala::FunctionContextImpl"* }%"class.impala::FunctionContextImpl" = type opaque%"struct.impala_udf::StringVal" = type { %"struct.impala_udf::AnyVal", i32, i8* }%"struct.impala_udf::AnyVal" = type { i8 }

; Function Attrs: nounwind readonly ssp uwtabledefine zeroext i1 @_Z8StringEqPN10impala_udf15FunctionContextERKNS_9StringValES4_(%"class.impala_udf::FunctionContext"* nocapture %context, %"struct.impala_udf::StringVal"* nocapture %arg1, %"struct.impala_udf::StringVal"* nocapture %arg2) #0 {entry: %is_null = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg1, i64 0, i32 0, i32 0 %0 = load i8* %is_null, align 1, !tbaa !0, !range !3 %is_null1 = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg2, i64 0, i32 0, i32 0 %1 = load i8* %is_null1, align 1, !tbaa !0, !range !3 %cmp = icmp eq i8 %0, %1 br i1 %cmp, label %if.end, label %return

if.end: ; preds = %entry %tobool = icmp eq i8 %0, 0 br i1 %tobool, label %if.end7, label %return

if.end7: ; preds = %if.end %len = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg1, i64 0, i32 1 %2 = load i32* %len, align 4, !tbaa !4 %len8 = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg2, i64 0, i32 1 %3 = load i32* %len8, align 4, !tbaa !4 %cmp9 = icmp eq i32 %2, %3 br i1 %cmp9, label %if.end11, label %return

if.end11: ; preds = %if.end7 %ptr = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg1, i64 0, i32 2 %4 = load i8** %ptr, align 8, !tbaa !5 %ptr12 = getelementptr inbounds %"struct.impala_udf::StringVal"* %arg2, i64 0, i32 2 %5 = load i8** %ptr12, align 8, !tbaa !5 %cmp13 = icmp eq i8* %4, %5 br i1 %cmp13, label %return, label %lor.rhs

lor.rhs: ; preds = %if.end11 %conv17 = sext i32 %2 to i64 %call = tail call i32 @memcmp(i8* %4, i8* %5, i64 %conv17) %cmp18 = icmp eq i32 %call, 0 br label %return

return: ; preds = %lor.rhs, %if.end11, %if.end7, %if.end, %entry %retval.0 = phi i1 [ false, %entry ], [ true, %if.end ], [ false, %if.end7 ], [ true, %if.end11 ], [ %cmp18, %lor.rhs ] ret i1 %retval.0}

; Function Attrs: nounwind readonlydeclare i32 @memcmp(i8* nocapture, i8* nocapture, i64) #1

attributes #0 = { nounwind readonly ssp uwtable "less-precise-fpmad"="false" "no-frame-pointer-elim"="true" "no-frame-pointer-elim-non-leaf"="true" "no-infs-fp-math"="false" "no-nans-fp-math"="false" "unsafe-fp-math"="false" "use-soft-float"="false" }attributes #1 = { nounwind readonly "less-precise-fpmad"="false" "no-frame-pointer-elim"="true" "no-frame-pointer-elim-non-leaf"="true" "no-infs-fp-math"="false" "no-nans-fp-math"="false" "unsafe-fp-math"="false" "use-soft-float"="false" }

!0 = metadata !{metadata !"bool", metadata !1}!1 = metadata !{metadata !"omnipotent char", metadata !2}!2 = metadata !{metadata !"Simple C/C++ TBAA"}!3 = metadata !{i8 0, i8 2}!4 = metadata !{metadata !"int", metadata !1}!5 = metadata !{metadata !"any pointer", metadata !1}

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Data type compatibility

struct AnyVal { bool is_null;};

struct StringVal : public AnyVal { int len; uint8_t* ptr;};

%AnyVal = type { i8 }%StringVal = type { %AnyVal, i32, i8* }

; or

%StringVal = type { { i8 }, i32, i8* }

C++

LLVM

IR

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Register and execute the function

CREATE FUNCTION StringEq(STRING, STRING) RETURNS BOOLEAN LOCATION '/path/to/bitcode.ll’ SYMBOL=’StringEq’;

SELECT StringEq(a, b) FROM mytable;

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Numba compiler

NumbaPython

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Impyla: Python Library for Impala

• pip install impyla• DB API v2.0 (PEP 249) compatible• Prototype sklearn API for Impala ML• Numba integration (described here)

• See blog post: http://blog.cloudera.com/blog/2014/04/a-new-python-client-for-impala/

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LLVM: Python example

@udf(IntVal(FunctionContext, StringVal))def hour_from_weird_date_format(context, date): return int(split(date, '-')[1])

ship_udf(cursor, hour_from_weird_data_format, '/path/to/store/udf.ll', 'my.impala.host')

cur.execute('SELECT hour_from_weird_data_format(date) ’ + ‘AS hour FROM mytable LIMIT 100’)

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Model Scoring: BigML on Census Data

MLaaS

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Model Scoring: BigML on Census Data

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Example: 100 Node Decision Treedef predict_income(impala_function_context, age, workclass, final_weight, education, education_num, marital_status, occupation, relationship, race, sex, hours_per_week, native_country, income): if (marital_status is None): return '<=50K' if (marital_status == 'Married-civ-spouse'): if (education_num is None): return '<=50K' if (education_num > 12): if (hours_per_week is None): return '>50K' if (hours_per_week > 31): if (age is None): return '>50K' if (age > 28): if (education_num > 13): if (age > 58): return '>50K' if (age <= 58): return '>50K' if (education_num <= 13): if (occupation is None): return '>50K' if (occupation == 'Exec-managerial'): return '>50K' if (occupation != 'Exec-managerial'): return '>50K' if (age <= 28): if (age > 24): if (occupation is None): return '<=50K' if (occupation == 'Tech-support'): return '>50K' if (occupation != 'Tech-support'): return '<=50K' if (age <= 24): if (final_weight is None): return '<=50K' if (final_weight > 492053): return '>50K' if (final_weight <= 492053): return '<=50K' if (hours_per_week <= 31): if (sex is None): return '<=50K' if (sex == 'Male'): if (age is None): return '<=50K' if (age > 29): if (age > 62): return '<=50K' if (age <= 62): return '<=50K' if (age <= 29): return '<=50K' if (sex != 'Male'): if (final_weight is None): return '>50K' if (final_weight > 264521): if (hours_per_week > 7): return '<=50K' if (hours_per_week <= 7): return '>50K' if (final_weight <= 264521): if (age is None): return '>50K' if (age > 26): return '>50K' if (age <= 26): return '<=50K' if (education_num <= 12): if (education_num > 8): if (age is None): return '<=50K' if (age > 35): if (hours_per_week is None): return '<=50K' if (hours_per_week > 33): if (education_num > 9): return '>50K' if (education_num <= 9): return '<=50K' if (hours_per_week <= 33): if (workclass is None): return '<=50K' if (workclass == 'Self-emp-inc'): return '>50K' if (workclass != 'Self-emp-inc'): return '<=50K' if (age <= 35): if (age > 24): if (occupation is None): return '<=50K' if (occupation == 'Exec-managerial'): return '<=50K' if (occupation != 'Exec-managerial'): return '<=50K' if (age <= 24): if (hours_per_week is None): return '<=50K' if (hours_per_week > 45): return '<=50K' if (hours_per_week <= 45): return '<=50K' if (education_num <= 8): if (age is None): return '<=50K' if (age > 36): if (hours_per_week is None): return '<=50K' if (hours_per_week > 22): if (education_num > 5): return '<=50K' if (education_num <= 5): return '<=50K' if (hours_per_week <= 22): return '<=50K' if (age <= 36): if (workclass is None): return '<=50K' if (workclass == 'Private'): if (age > 35): return '<=50K' if (age <= 35): return '<=50K' if (workclass != 'Private'): if (occupation is None): return '<=50K' if (occupation == 'Machine-op-inspct'): return '>50K' if (occupation != 'Machine-op-inspct'): return '<=50K' if (marital_status != 'Married-civ-spouse'): if (education_num is None): return '<=50K' if (education_num > 12): if (age is None): return '<=50K' if (age > 27): if (hours_per_week is None): return '<=50K' if (hours_per_week > 43): if (occupation is None): return '<=50K' if (occupation == 'Exec-managerial'): if (age > 41): return '>50K' if (age <= 41): return '<=50K' if (occupation != 'Exec-managerial'): if (education_num > 14): return '>50K' if (education_num <= 14): return '<=50K' if (hours_per_week <= 43): if (education_num > 14): if (age > 32): return '>50K' if (age <= 32): return '<=50K' if (education_num <= 14): if (age > 45): return '<=50K' if (age <= 45): return '<=50K' if (age <= 27): if (hours_per_week is None): return '<=50K' if (hours_per_week > 38): if (relationship is None): return '<=50K' if (relationship == 'Wife'): return '>50K' if (relationship != 'Wife'): if (hours_per_week > 77): return '<=50K' if (hours_per_week <= 77): return '<=50K' if (hours_per_week <= 38): return '<=50K' if (education_num <= 12): if (age is None): return '<=50K' if (age > 31): if (hours_per_week is None): return '<=50K' if (hours_per_week > 41): if (education_num > 5): if (age > 53): return '<=50K' if (age <= 53): return '<=50K' if (education_num <= 5): return '<=50K' if (hours_per_week <= 41): if (occupation is None): return '<=50K' if (occupation == 'Other-service'): if (relationship is None): return '<=50K' if (relationship == 'Wife'): return '<=50K' if (relationship != 'Wife'): return '<=50K' if (occupation != 'Other-service'): if (occupation == 'Machine-op-inspct'): return '<=50K' if (occupation != 'Machine-op-inspct'): return '<=50K' if (age <= 31): if (age > 21): if (hours_per_week is None): return '<=50K' if (hours_per_week > 41): if (workclass is None): return '<=50K' if (workclass == 'Private'): return '<=50K' if (workclass != 'Private'): return '<=50K' if (hours_per_week <= 41): if (education_num > 9): return '<=50K' if (education_num <= 9): return '<=50K' if (age <= 21): if (education is None): return '<=50K' if (education == '7th-8th'): if (occupation is None): return '<=50K' if (occupation == 'Other-service'): return '<=50K' if (occupation != 'Other-service'): return '<=50K' if (education != '7th-8th'): return '<=50K'

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Batch Scoring with PySpark

# parse the text dataobservations = sc.textFile('/path/to/census_data').map(parse_obs)

# perform batch scoringpredictions = observations.map(lambda tup: predict_income(*tup))

# trigger computationdistinct = predictions.distinct().collect()

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Batch Scoring with Impala

# compile the scoring functionpredict_income = udf(signature)(predict_income)

ship_udf(cursor, predict_income, ...)

# perform batch scoringcursor.execute(‘SELECT DISTINCT predict_income(age, ... ) ‘ + ‘FROM census_text’)distinct = cursor.fetchall()

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Execution Time

execution_time =

per_job_overhead +

N * ( per_record_exec + memcmp_exec )

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PySpark vs. Impala Performance

Tree size (nodes)

Spark execution time (s)

Impala execution time (s)

Fold difference

Impala compilation time (s)

Bytecode size (bytes)

Percent memcmp nodes

0 160 9 17x 0 4

100 175 22 8x 1 2254 22%

500 178 27 7x 4 9803 35%

1000 184 32 6x 16 23495 34%

1500 188 35 5x 18 28301 34%

2000 196 37 5x 31 42442 33%

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Execution Time

execution_time =

per_job_overhead +

N * ( per_record_exec + memcmp_exec )

Spark: 24 threads / node

[ ]

Impala: 1 thread / node

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PySpark vs. Impala Performance

Tree size (nodes)

Spark execution time (s)

Impala execution time (s)

Fold difference

Impala compilation time (s)

Bytecode size (bytes)

Percent memcmp nodes

0 160 9 17x 0 4

100 175 22 8x 1 2254 22%

500 178 27 7x 4 9803 35%

1000 184 32 6x 16 23495 34%

1500 188 35 5x 18 28301 34%

2000 196 37 5x 31 42442 33%

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Current Status

• Support for all Impala UDF data types (e.g., IntVal, StringVal, etc.)

• Support for casts to/from primitive types:• Any operations on primitives should work on Impala types• Support for NULL types as Python None

• Proof-of-principle support for Python string module• len• split• Concatenation

• Call out to any extern C functions• Proposed directions

• Array handling• Numpy support• What else?

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UDFs with Impala + Numba

• Simplicity of Python interface/syntax• Performance of compiled language like C++

• Developed at: https://github.com/cloudera/impyla• Please try it and tell us what features would be useful• Please contribute!

pip install impyla

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