The document compares different big data analytics frameworks that use the directed acyclic graph (DAG) model versus the traditional MapReduce (MR) model. It finds that while DAG frameworks are improving and Spark comes closest to meeting diverse big data use cases that go beyond batch analytics, DAG runtimes are still maturing and have limitations compared to MR in areas like completeness, performance, and reliability. The document concludes that while Hadoop may no longer be the only option, frameworks still need improvements for "no-MR" to fully replace MR for big data processing.

1. N(ot)-o(nly)-(Ha)doop - the DAG showdown
Intel Corporation
Joydeep Ghosh & Seshu Edala
June, 2015

2. Copyright © 2015, Intel Corporation. All rights reserved.
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THIS SUMMARY.
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microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer
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Copyright © 2015, Intel Corporation. All rights reserved.
2

3. Copyright © 2015, Intel Corporation. All rights reserved.
Key Messages
3
 Evolution of Hadoop to Big Data
 Introduction to DAG
 DAG runtimes
 Evaluation
 Performance
 Completeness
 Results
nodoop is still not-only-hadoop; time for “no-MR” looks closer

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4
slow
fragmented
skills gap
block-oriented
data mutability
+

5. Copyright © 2015, Intel Corporation. All rights reserved.
Hadoop to Big Data
5
Processing Model
Analytical Model
Storage Model
Language Model
Complex EventBatch In-Memory
Machine
Learning
Textual SpatialAggregate Temporal Graph
Unstructured Relational Columnar Hierarchic Graph
MR SQL NOSQL JSQL NOSPARQL
retrofitting Hadoop [unstructured batch analytics] to cater to the full big data demand

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Map Reduce (MR) and Directed Acyclic Graph
(DAG)
6
Stage - 1
Stage - 2
Stage - 3
 continuous dataflow
 relational semantics
 in-memory buffering
 sequential dataflow
 MR semantics
 on-disk storage

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MR & DAG Runtimes
7
* Chose only few products for evaluation
DAG*MR
Note: Other names and brands may be claimed as the property of others.
Impala
Hadoop 2.5.0-cdh5.3.0, Hive 0.13.1-cdh5.3.0,presto-server-0.103,
Apache Drill: 0.9.0 ,impalad version 2.1.0-cdh5, Spark 1.3.1, HPCC –
5.0.14.1

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Completeness Criteria
 On Disk failover
 HDFS Compatibility
 Yarn Integration
 File formats
 Expressive language
 Streaming support
8
 Connectivity
 Web UI
 Integrated Monitoring
 Security
 Hybrid Analytics
 Seamless Dataframes

9. Copyright © 2015, Intel Corporation. All rights reserved.
Completeness Scores
9
Note: Other names and brands may be claimed as the property of others.

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Performance Criteria
10
0
200
400
600
800
1000
1200
1400
1600
1800
FULL TABLE
SCAN
JOIN FACT
DIMENSION
AGGREGATE
FUNCTION
JOIN FACT TO
FACT
TEXT
ANALYTICS
LOG ANALYTICS
PROCESSINGTIMESECONDS
PERFORMANCE COMPARISION
Hive
Impala
Spark
Drill
Presto
HPCC

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11
 All queries completed successfully
 A reliable baseline
670.99
640.37
1705.75
983.73
1298.56
411.88
F ULL T A BLE
S C A N
JOIN F A C T
DIME NSION
A GGRE GA T E
FUNC T ION
JOIN F A C T
T O FAC T
T E X T
ANALY T IC S
LOG
ANALY T IC S
HIVEPROCESSINGTIMESECONDS
Hive
Impala
Spark
Drill
Presto
HPCC

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12
 All queries completed successfully
 Lack of window functions in Spark-
SQL makes moving average analytics
challenging
 Mixed SQL & RDD programming
 Not DAG!
 ~2x to 8x
87.28
192.88
669.09
231.55
132.05
285
F ULL T A BLE
S C A N
JOIN F A C T
DIME NSION
A GGRE GA T E
FUNC T ION
JOIN F A C T
T O FAC T
T E X T
ANALY T IC S
LOG
ANALY T IC S
SPARKPROCESSINGTIMESECONDS
Spark
Impala
Hive
Drill
Presto
HPCC

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13
 In-memory DAG
 Table generating functions and array
functions are not supported; text
analytics example failed
 ~1x to 20x
29.06
72.45
222.98
168.86
0
747.64
F ULL T A BLE
S C A N
JOIN F A C T
DIME NSION
A GGRE GA T E
FUNC T ION
JOIN F A C T
T O FAC T
T E X T
A NA LY T IC S
LOG
ANALY T IC S
IMPALAPROCESSINGTIMESECONDS
Impala
Hive
Spark
Drill
Presto
HPCC

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14
 In-memory DAG – No Resilience
 Table generating functions not
supported; text analytics example failed
 Window functions are still
beta/unsupported; log analytics failed
 ~ 5x to 50x
126.99
83.97
250.19
15.13
0
0
F ULL T A BLE
S C A N
JOIN F A C T
DIME NSION
A GGRE GA T E
FUNC T ION
JOIN F A C T
T O FAC T
T E X T
ANALY T IC S
LOG
ANALY T IC S
DRILLPROCESSINGTIMESECONDS
Drill
Spark
Impala
Hive
Presto
HPCC

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15
 In-memory DAG – No Resilience
 Table generating functions not
supported; text analytics example
failed
 ~ 5x to 60x
4.69
67
491
233.66
0
89.66
F ULL T A BLE
S C A N
JOIN F A C T
DIME NS ION
A GGRE GA T E
F UNC T ION
JOIN F A C T
T O F A C T
T E X T
A NA LY T IC S
LOG
A NA LY T IC S
PRESTOPROCESSINGTIMESECONDS
Presto
Drill
Spark
Impala
Hive
HPCC

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16
 All queries completed successfully
 On-disk DAG runtime; reliable,
complete, performant
 Declarative ECL language; not SQL
 No native support for HDFS
 ~ 2x to 20x
39.43
51.16
305.5
10.43
315.5
206.1
F ULL T A BLE
S C A N
JOIN F A C T
DIME NSION
A GGRE GA T E
FUNC T ION
JOIN F A C T
T O FAC T
T E X T
ANALY T IC S
LOG
ANALY T IC S
HPCCPROCESSINGTIMESECONDS
HPCC
Presto
Drill
Spark
Impala
Hive

17. Copyright © 2015, Intel Corporation. All rights reserved.
Findings
 Big data use-cases stretch beyond unstructured batch jobs.
 Can DAG meet the demand and performance?
17
Problem Context
 DAG runtimes are still maturing
 Spark comes closest

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NODOOP = Not only Hadoop
18

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19
Questions

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21
Backup

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Benchmark Environment
 Cloudera Enterprise 5.3.2
 4 Node Cluster [1 master + 3 workers]
 Memory 62.9 GiB in each node
 Cores 16
 TPCDS Database with Scale of 250
 Queries used
 Full Table Scan
 Fact and Dimension Join
 Aggregate functions
 Fact to Fact Join
 Text Analytics
 Log Analytics
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 Hadoop 2.5.0-cdh5.3.0
 Hive 0.13.1-cdh5.3.0
 presto-server-0.103
 Apache Drill: 0.9.0
 impalad version 2.1.0-cdh5
 Spark 1.3.1
 HPCC – 5.0.14.1
 TPCDS Scale of 250 – 19.3 GB
 Store Sales -18.8 GB
 Customer - 300.3 MB
 Text Analytics (twitter) – 436.6 MB
 CIKM twitter dataset
 Log Analytics (weblog) - 5.0 GB
 HPCC ECL WLAM sample
Versions
Data Volume

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Completeness Scores
23
To-disk failover 2 3 0 3 0 3 4
HDFS Compatibility 4 4 4 4 4 4 2
Yarn Integration 4 0 0 3 1 4 0
File formats 4 4 4 3 2 4 1
Expressive language 3 3 3 4 3 3 3
Streaming support 0 0 0 4 0 4 0
Connectivity 4 4 4 4 4 2 3
Web UI 2 3 4 4 4 3 3
Integrated Monitoring 2 3 4 4 4 3 4
Security 3 3 1 1 1 1 1
Hybrid Analytics 3 2 1 4 1 3 4
Seamless Dataframes 1 1 1 4 1 4 2
32 30 26 42 25 38 25
*Score: 0 Min [0] - 4 Max [4]Note: Other names and brands may be claimed as the property of others.