Approximate Queries on Very Large Data UC Berkeley Sameer Agarwal Joint work with Ariel Kleiner,...

Approximate Queries on Very Large Data

UC Berkeley

Sameer AgarwalJoint work with Ariel Kleiner, Henry Milner, Barzan Mozafari, Ameet Talwalkar, Michael Jordan, Samuel Madden, Ion Stoica

Our GoalSupport interactive SQL-like aggregate queries over massive sets of data

Our GoalSupport interactive SQL-like aggregate queries over massive sets of data

blinkdb> SELECT AVG(jobtime)

FROM very_big_log AVG, COUNT, SUM, STDEV, PERCENTILE

etc.

Support interactive SQL-like aggregate queries over massive sets of data


FROM very_big_log

WHERE src = ‘hadoop’

FILTERS, GROUP BY clauses

Our Goal



FROM very_big_log

WHERE src = ‘hadoop’ LEFT OUTER JOIN logs2

ON very_big_log.id = logs.id

JOINS, Nested Queries etc.

Our Goal


blinkdb> SELECT my_function(jobtime)

FROM very_big_log

WHERE src = ‘hadoop’ LEFT OUTER JOIN logs2

ON very_big_log.id = logs.id

ML Primitives,User Defined Functions

Our Goal

Hard Disks

½ - 1 Hour 1 - 5 Minutes 1 second

?Memory

100 TB on 1000 machines

Query Execution on Samples

ID

City Salary

1 NYC 50,000

2 NYC 62,492

3 Berkeley 78,212

4 NYC 120,242

5 NYC 98,341

6 Berkeley 75,453

7 NYC 60,000

8 NYC 72,492

9 Berkeley 88,212

10

Berkeley 92,242

11

NYC 70,000

12

Berkeley 102,492

Query Execution on SamplesWhat is the average Salary of all the people in the table?

$80,848

ID

City Salary

1 NYC 50,000

2 NYC 62,492

3 Berkeley 78,212

4 NYC 120,242

5 NYC 98,341

6 Berkeley 75,453

7 NYC 60,000

8 NYC 72,492

9 Berkeley 88,212

10

Berkeley 92,242

11

NYC 70,000

12

Berkeley 102,492


ID City Salary

Sampling Rate

2 NYC 62,492 1/4

6 Berkeley

75,453 1/4

8 NYC 72,492 1/4

UniformSample

$70,145$80,848

ID

City Salary

1 NYC 50,000

2 NYC 62,492

3 Berkeley 78,212

4 NYC 120,242

5 NYC 98,341

6 Berkeley 75,453

7 NYC 60,000

8 NYC 72,492

9 Berkeley 88,212

10

Berkeley 92,242

11

NYC 70,000

12

Berkeley 102,492


ID City Salary

Sampling Rate

2 NYC 62,492 1/4

6 Berkeley

75,453 1/4

8 NYC 72,492 1/4

UniformSample

$70,145 +/- 10,815

$80,848

ID

City Salary

1 NYC 50,000

2 NYC 62,492

3 Berkeley 78,212

4 NYC 120,242

5 NYC 98,341

6 Berkeley 75,453

7 NYC 60,000

8 NYC 72,492

9 Berkeley 88,212

10

Berkeley 92,242

11

NYC 70,000

12

Berkeley 102,492

Query Execution on SamplesWhat is the average Salary of all the people in the table?ID City Salar

ySampling Rate

2 NYC 62,492 1/2

3 Berkeley

78,212 1/2

5 NYC 60,000 1/2

6 Berkeley

75,453 1/2

8 NYC 72,492 1/2

12 Berkeley

102,492

1/2

UniformSample

$75,190 +/- 5,895

$80,848$70,145 +/- 10,815

Speed/Accuracy Trade-off

Execution Time

Erro

r

30 mins

Time to Execute on

Entire Dataset

InteractiveQueries

5 sec

Execution Time

Erro

r

30 mins

Time to Execute on

Entire Dataset

InteractiveQueries

5 sec

Speed/Accuracy Trade-off

Pre-ExistingNoise

What is BlinkDB?A data analysis (warehouse) system that …

- builds on Shark and Spark

- returns fast, approximate answers with error bars by executing queries on small samples of data

- is compatible with Apache Hive (storage, serdes, UDFs, types, metadata) and supports Hive’s SQL-like query structure with minor modifications

Sampling Vs. No Sampling

0100200300400500600700800900

1000

1 10-1 10-2 10-3 10-4 10-5

Fraction of full data

Que

ry R

espo

nse

Tim

e (S

econ

ds)

103

1020

18 13 10 8

10x as response timeis dominated by I/O

Sampling Vs. No Sampling

0100200300400500600700800900

1000

1 10-1 10-2 10-3 10-4 10-5

Fraction of full data

Que

ry R

espo

nse

Tim

e (S

econ

ds)

103

1020

18 13 10 8

(0.02%)(0.07%) (1.1%) (3.4%) (11%)

Error Bars

Hive Architecture

Hadoop Storage (e.g., HDFS, HBase)

Metastore

MapReduce

SQL Parser

Query Optimize

r

Physical Plan

SerDes, UDFs

Execution

Driver

Command-line Shell Thrift/JDBC

Shark Architecture


Metastore

Spark

SQL Parser

Query Optimize

r

Physical Plan

SerDes, UDFs

Execution

Driver


BlinkDB Architecture


Metastore

Spark

SQL Parser

Query Optimize

r

Physical Plan

SerDes, UDFs

Execution

Driver


BlinkDB alpha-0.1.0

1. Released and available at http://blinkdb.org

2. Allows you to create random and stratified samples on native tables and materialized views

3. Adds approximate aggregate functions with statistical closed forms to HiveQL : approx_avg(), approx_sum(), approx_count() etc.

http://blinkdb.org/

Example: Preparing the Datablinkdb>

blinkdb> create external table logs (dt string, event string, bytes int) row format delimited fields terminated by ' ' location ’/tmp/logs’;

Referencing an external table logs in BlinkDB

Example: Preparing the Data

blinkdb> create external table logs (dt string, event string, bytes int) row format delimited fields terminated by ' ' location ’/tmp/logs';

blinkdb> create table logs_sample as select * from logs samplewith 0.01;

Create a 1% random sample logs_sample from logs


blinkdb> create external table logs (dt string, event string, bytes int) row format delimited fields terminated by ' ' location ’/tmp/logs';

blinkdb> create table logs_sample as select * from logs samplewith 0.01;

blinkdb> create table logs_sample_cached as select * from logs_sample;

Supports all Shark primitives for caching samples in memory


blinkdb> set blinkdb.sample.size=32810

blinkdb> set blinkdb.dataset.size=3198910

Giving BlinkDB information about the size of sample you wish to operate on and the size of the original dataset

Example: Analyzing the Data



blinkdb> select approx_count(1) from logs_sample_cached where event = “foo”;


Prefixing approx_ to an aggregate operator tells BlinkDB to return an approximate answer



blinkdb> select approx_count(1) from logs_sample_cached where event = “foo”;

12810132 +/- 3423 (99% Confidence)


Returns an approximate answer with an error bar and confidence interval

blinkdb> create table logs_sample as select * from [any subquery] samplewith 0.01;

Example: There’s more!

The sample operator can be anywhere in the query graph


blinkdb> select approx_count(1) from logs_sample_cached where event = “foo” GROUP BY dt ORDER BY dt;


Retains remaining Hive Query Structure


blinkdb> select approx_count(1) from logs_sample_cached where event = “foo” GROUP BY dt ORDER BY dt;

12810132 +/- 3423 (99% Confidence)


Note: The output is a String

Feature Roadmap1. Integrating BlinkDB with Shark as an

experimental feature (coming soon!)

2. Automatic Sample Management

3. More Hive Aggregates, UDAF Support

4. Runtime Correctness Tests

SELECT avg(sessionTime) FROM Table WHERE city=‘San Francisco’WITHIN 1 SECONDS 234.23 ± 15.32

Automatic Sample Management

Goal: The API should abstract the details of creating, deleting and managing samples from the user

SELECT avg(sessionTime) FROM Table WHERE city=‘San Francisco’WITHIN 2 SECONDS 234.23 ± 15.32


239.46 ± 4.96


SELECT avg(sessionTime) FROM Table WHERE city=‘San Francisco’ERROR 0.1 CONFIDENCE 95.0%



TABLE

Sam

plin

g M

odul

e

Original Data

Offline-sampling: Creates an optimal set of samples on native tables and materialized views based on query history and workload characteristics


TABLE

Sam

plin

g M

odul

e

In-MemorySamples

On-DiskSamples

Original Data

Sample Placement: Samples striped over 100s or 1,000s of machines both on disks and in-memory.


SELECT foo (*)FROM TABLE

WITHIN 2

Query Plan

HiveQL/SQLQuery

Sample Selection

TABLE

Sam

plin

g M

odul

e

In-MemorySamples

On-DiskSamples

Original Data



WITHIN 2

Query Plan

HiveQL/SQLQuery

Sample Selection

TABLE

Sam

plin

g M

odul

e

In-MemorySamples

On-DiskSamples

Original Data

Online sample selection to pick best sample(s) based on query latency and accuracy requirements


TABLE

Sam

plin

g M

odul

e

In-MemorySamples

On-DiskSamples

Original Data

Shark


WITHIN 2

New Query Plan

HiveQL/SQLQuery

Sample Selection

Error Bars & Confidence Intervals

Result182.23 ± 5.56

(95% confidence)

Parallel query execution on multiple samples striped across multiple machines.


1. Using Bootstrap to estimate error

More Aggregates/ UDAFs Support

Sample

A



Sample

A A1 A2 An

…

…

Bootstrap Operator



Sample

A A1 A2 An

…

…

Placement of the Bootstrap Operator in the query graph is critical to performance



Sample

A A1 A2 An

…

…

However, the bootstrap can fail

1. Given a query,how do you know if it can be approximated at runtime?- Depends on the query, data distribution, and

sample size

2. Need for runtime diagnosis tests- Check whether error improves as sample size

increases- 30,000 extremely small query tasks

Runtime Correctness Tests

1. BlinkDB alpha-0.1.0 released and available at http://blinkdb.org

2. Takes just 5-10 minutes to run it locally or to spin an EC2 cluster

3. Hands-on Exercises today at the AMPCamp

4. Designed to be a drop-in tool like Shark

Getting Started

http://blinkdb.org/

http://blinkdb.org/

1. Approximate queries is an important means to achieve interactivity in processing large datasets

2. BlinkDB..- builds on Shark and Spark- approximate answers with error bars by executing queries on

small samples of data- supports existing Hive Query with minor modifications

3. For more information, please check out our EuroSys 2013 (http://bit.ly/blinkdb-1) and KDD 2014 (http://bit.ly/blinkdb-2) papers

Summary

Thanks!

http://bit.ly/blinkdb-1

http://bit.ly/blinkdb-2

Approximate Queries on Very Large Data UC Berkeley Sameer Agarwal Joint work with Ariel Kleiner,...

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