1. 1
Compiled
Python
UDFs
for
Impala
Uri
Laserson
20
May
2014

2. Impala
User-­‐defined
FuncAons
(UDFs)
• Tuple
=>
Scalar
value
• Substring
• sin,
cos,
pow,
…
• Machine-­‐learning
models
• Supports
Hive
UDFs
(Java)
• RelaAvely
unpleasurable
• Slower
• Impala
(naAve)
UDFs
• C++
interface
designed
for
efficiency
• Similar
to
Postgres
UDFs
• Runs
any
LLVM-­‐compiled
code
2

3. LLVM
compiler
infrastructure
3

4. LLVM:
C++
example
4
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;!
}!

5. LLVM:
IR
output
5
; 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 uwtable!
define 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 ]!

6. LLVM:
IR
output
6
; 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 uwtable!
define 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 ]!

7. Data
type
compaAbility
7
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

8. Register
and
execute
the
funcAon
8
CREATE FUNCTION StringEq(STRING, STRING)!
RETURNS BOOLEAN!
LOCATION '/path/to/bitcode.ll’!
SYMBOL=’StringEq’;!
SELECT StringEq(a, b) FROM mytable;!

9. Numba
compiler
9
NumbaPython

10. Impyla:
Python
Library
for
Impala
• pip
install
impyla
• DB
API
v2.0
(PEP
249)
compaAble
• Prototype
sklearn
API
for
Impala
ML
• Numba
integraAon
(described
here)
• See
blog
post:
h]p://blog.cloudera.com/blog/
2014/04/a-­‐new-­‐python-­‐client-­‐for-­‐impala/
10

11. LLVM:
Python
example
11
@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’)!

12. Model
Scoring:
BigML
on
Census
Data
12
MLaaS

13. Model
Scoring:
BigML
on
Census
Data
13

14. Example:
100
Node
Decision
Tree
14
def 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):!

15. Batch
Scoring
with
PySpark
15
# parse the text data!
observations = sc.textFile('/path/to/census_data').map(parse_obs)!
!
# perform batch scoring!
predictions = observations.map(lambda tup: predict_income(*tup))!
!
# trigger computation!
distinct = predictions.distinct().collect()!
!

16. Batch
Scoring
with
Impala
16
# compile the scoring function!
predict_income = udf(signature)(predict_income)!
!
ship_udf(cursor, predict_income, ...)!
!
# perform batch scoring!
cursor.execute(‘SELECT DISTINCT predict_income(age, ... ) ‘ +!
‘FROM census_text’)!
distinct = cursor.fetchall()!
!

17. ExecuAon
Time
17
execution_time =!
!
per_job_overhead +!
!
N * ( per_record_exec + memcmp_exec )!

18. PySpark
vs.
Impala
Performance
18
Tree
size
(nodes)
Spark
execu?on
?me
(s)
Impala
execu?on
?me
(s)
Fold
differenc
e
Impala
compila?
on
?me
(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%

19. ExecuAon
Time
19
execution_time =!
!
per_job_overhead +!
!
N * ( per_record_exec + memcmp_exec )!
Spark:
24
threads
/
node
[
]
Impala:
1
thread
/
node

20. PySpark
vs.
Impala
Performance
20
Tree
size
(nodes)
Spark
execu?on
?me
(s)
Impala
execu?on
?me
(s)
Fold
differenc
e
Impala
compila?
on
?me
(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%

21. Current
Status
• Support
for
all
Impala
UDF
data
types
(e.g.,
IntVal,
StringVal,
etc.)
• Support
for
casts
to/from
primiAve
types:
• Any
operaAons
on
primiAves
should
work
on
Impala
types
• Support
for
NULL
types
as
Python
None!
• Proof-­‐of-­‐principle
support
for
Python
string
module
• len!
• split!
• ConcatenaAon
• Call
out
to
any
extern C
funcAons
• Proposed
direcAons
• Array
handling
• Numpy
support
• What
else?
21

22. UDFs
with
Impala
+
Numba
• Simplicity
of
Python
interface/syntax
• Performance
of
compiled
language
like
C++
• Developed
at:
h]ps://github.com/cloudera/impyla
• Please
try
it
and
tell
us
what
features
would
be
useful
• Please
contribute!
22
pip install impyla!

23. 23