The Columnar Era: Leveraging Parquet, Arrow and Kudu for High-Performance Analytics

1. © 2016 Dremio Corporation @DremioHQ
The Columnar Era: Leveraging Parquet,
Arrow and Kudu for High-Performance
Analytics
Julien Le Dem
Principal Architect, Dremio
VP Apache Parquet, Apache Arrow PMC

2. © 2016 Dremio Corporation @DremioHQ
• Architect at @DremioHQ
• Formerly Tech Lead at Twitter on Data Platforms.
• Creator of Parquet
• Apache member
• Apache PMCs: Arrow, Incubator, Pig, Parquet
Julien Le Dem
@J_ Julien

3. © 2016 Dremio Corporation @DremioHQ
Agenda
• Benefits of Columnar representation
– Immutable On disk (Apache Parquet)
– Mutable on disk (Apache Kudu)
– In memory (Apache Arrow)
• Community Driven Standard
• Interoperability and Ecosystem

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Benefits of Columnar formats
@EmrgencyKittens

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Columnar layout
Logical table
representation
Row layout
Column layout

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Mutable or Immutable Storage
• Different trade offs
– Immutable: (Parquet).
• Higher write throughput (no random modification after completion).
• Easy to share, replicate, access concurrently.
• Modifications require rewrite of dataset.
• No operational overhead (no extra service, just your file system)
– Mutable: (Kudu)
• More flexible trade off between update speed and read speed.
• Low-latency for short accesses (primary key indexes and quorum
replication)
• Database-like semantics (initially single-row ACID)
• Needs to be managed (new daemon).

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On Disk and in Memory
• Different trade offs
– On disk: Storage.
• Accessed by multiple queries.
• Priority to I/O reduction (but still needs good CPU throughput).
• Mostly Streaming access.
– In memory: Transient.
• Specific to one query execution.
• Priority to CPU throughput (but still needs good I/O).
• Streaming and Random access.

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Parquet on disk columnar format

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Parquet on disk columnar format
• Nested data structures
• Compact format:
– type aware encodings
– better compression
• Optimized I/O:
– Projection push down (column pruning)
– Predicate push down (filters based on stats)

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Access only the data you need
a b c
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4
a5 b5 c5
a b c
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4
a5 b5 c5
a b c
a1 b1 c1
a2 b2 c2
a3 b3 c3
a4 b4 c4
a5 b5 c5
+ =
Columnar Statistics
Read only the
data you need!

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Parquet nested representation
Document
DocId Links Name
Backward Forward Language Url
Code Country
Columns:
docid
links.backward
links.forward
name.language.code
name.language.country
name.url
Borrowed from the Google Dremel paper
https://blog.twitter.com/2013/dremel-made-simple-with-parquet

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Kudu data representation

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Kudu Tablets
• Typed columns
• Inserts buffered in an in-memory store (like HBase’s memstore)
• Flushed to disk: Columnar layout, similar to Apache Parquet
• Updates use MVCC (updates tagged with timestamp, not in-place)
– Allow “SELECT AS OF <timestamp>” queries and consistent cross-tablet
scans
• Near-optimal read path for “current time” scans
– No per row branches, fast vectorized decoding and predicate evaluation
• Performance worsens based on number of recent updates

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Kudu
• High throughput for big scans (columnar
storage and replication)
– Goal: Within 2x of Parquet
• Low-latency for short accesses (primary key
indexes and quorum replication)
– Goal: 1ms read/write on SSD
• Database-like semantics (initially single-row
ACID)
• Relational data model
– SQL query
– “NoSQL” style scan/insert/update (Java client)
Parquet

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LSM vs Kudu
• LSM – Log Structured Merge (Cassandra, HBase, etc)
– Inserts and updates all go to an in-memory map (MemStore)
and later flush to on-disk files (HFile/SSTable)
– Reads perform an on-the-fly merge of all on-disk HFiles
• Kudu
– Shares some traits (memstores, compactions)
– More complex.
– Slower writes in exchange for faster reads (especially scans)
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Kudu trade-offs: write
• Batch inserts are slower than Parquet
– Extra bloom filter lookup per insert
• Random updates are slower than HBase
– HBase model allows random updates without
incurring a disk seek
– Kudu requires a key lookup before update, bloom
lookup before insert
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Kudu trade-offs: read
• Scan speed is close to Parquet and faster than HBase
– Columnar on disk like Parquet
– Only one DiskRowSet contains updates for a given row. Fewer files
lookup than Hbase (but more than Parquet).
• Single-row reads may be slower than Hbase (and both are
faster than Parquet)
– Columnar design is optimized for scans
– Future: may introduce “column groups” for applications where
single-row access is more important
– Especially slow at reading a row that has had many recent updates
(e.g YCSB “zipfian”)
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Kudu is…
– NOT a SQL database
• “Bring Your Own SQL”
– NOT a filesystem
• data must have tabular structure
– NOT an in-memory database
• Very fast for memory-sized workloads, but can operate on
larger data too
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Arrow in memory columnar format

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Arrow goals
• Well-documented and cross language
compatible
• Designed to take advantage of modern CPU
characteristics
• Embeddable in execution engines, storage
layers, etc.
• Interoperable

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Arrow in memory columnar format
• Nested Data Structures
• Maximize CPU throughput
– Pipelining
– SIMD
– cache locality
• Scatter/gather I/O

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CPU pipeline

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Minimize CPU cache misses
a cache miss costs 10 to 100s cycles depending on the level

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Focus on CPU Efficiency
Traditional
Memory Buffer
Arrow
Memory Buffer
• Cache Locality
• Super-scalar & vectorized
operation
• Minimal Structure Overhead
• Constant value access
– With minimal structure
overhead
• Operate directly on columnar
compressed data

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Columnar data
persons = [{
name: ’Joe',
age: 18,
phones: [
‘555-111-1111’,
‘555-222-2222’
]
}, {
name: ’Jack',
age: 37,
phones: [ ‘555-333-3333’ ]
}]

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Java: Memory Management
• Chunk-based managed allocator
– Built on top of Netty’s JEMalloc implementation
• Create a tree of allocators
– Limit and transfer semantics across allocators
– Leak detection and location accounting
• Wrap native memory from other applications

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Arrow RPC & IPC

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Common Message Pattern
• Schema Negotiation
– Logical Description of structure
– Identification of dictionary encoded
Nodes
• Dictionary Batch
– Dictionary ID, Values
• Record Batch
– Batches of records up to 64K
– Leaf nodes up to 2B values
Schema
Negotiation
Dictionary
Batch
Record
Batch
Record
Batch
Record
Batch
1..N
Batches
0..N
Batches

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Record Batch Construction
Schema
Negotiation
Dictionary
Batch
Record
Batch
Record
Batch
Record
Batch
name (offset)
name (data)
age (data)
phones (list offset)
phones (data)
data header (describes offsets into data)
name (bitmap)
age (bitmap)
phones (bitmap)
phones (offset)
{
name: ’Joe',
age: 18,
phones: [
‘555-111-1111’,
‘555-222-2222’
]
}
Each box (vector) is contiguous memory
The entire record batch is contiguous on wire

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Moving Data Between Systems
RPC
• Avoid Serialization & Deserialization
• Layer TBD: Focused on supporting vectored io
– Scatter/gather reads/writes against socket
IPC
• Alpha implementation using memory mapped files
– Moving data between Python and Drill
• Working on shared allocation approach
– Shared reference counting and well-defined ownership semantics

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Shared Need => Open Source Opportunity
“We are also considering switching to
a columnar canonical in-memory
format for data that needs to be
materialized during query processing,
in order to take advantage of SIMD
instructions” -Impala Team
“A large fraction of the CPU time is spent
waiting for data to be fetched from main
memory…we are designing cache-friendly
algorithms and data structures so Spark
applications will spend less time waiting to
fetch data from memory and more time
doing useful work” – Spark Team
“Drill provides a flexible hierarchical
columnar data model that can
represent complex, highly dynamic
and evolving data models and allows
efficient processing of it without need
to flatten or materialize.” -Drill Team

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Community Driven Standard

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An open source standard
• Parquet: Common need for on disk columnar.
• Arrow: Common need for in memory columnar.
• Arrow building on the success of Parquet.
• Benefits:
– Share the effort
– Create an ecosystem
• Standard from the start

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The Apache Arrow Project
• New Top-level Apache Software Foundation project
– Announced Feb 17, 2016
• Focused on Columnar In-Memory Analytics
1. 10-100x speedup on many workloads
2. Common data layer enables companies to choose best of
breed systems
3. Designed to work with any programming language
4. Support for both relational and complex data as-is
• Developers from 13+ major open source projects involved
– A significant % of the world’s data will be processed through
Arrow!
Calcite
Cassandra
Deeplearning4j
Drill
Hadoop
HBase
Ibis
Impala
Kudu
Pandas
Parquet
Phoenix
Spark
Storm
R

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Interoperability and Ecosystem

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High Performance Sharing & Interchange
Today With Arrow
• Each system has its own internal memory
format
• 70-80% CPU wasted on serialization and
deserialization
• Functionality duplication and unnecessary
conversions
• All systems utilize the same memory
format
• No overhead for cross-system
communication
• Projects can share functionality (eg:
Parquet-to-Arrow reader)

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Language Bindings
Parquet
• Target Languages
– Java
– CPP (underway)
– Python & Pandas
(underway)
• Engines integration:
– Faster to list those
who don’t support it
Arrow
• Target Languages
– Java (beta)
– CPP (underway)
– Python & Pandas (underway)
– R
– Julia
• Initial Focus
– Read a structure
– Write a structure
– Manage Memory
Kudu
• Target Languages
– Java
– CPP
• Engines integration:
– MapReduce,
– Spark
– Impala
– Drill

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Example data exchanges:

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RPC: Query execution
The memory
representation is sent
over the wire.
No serialization
overhead.
Scanner
Scanner
Scanner
Parquet files
projection push down
read only a and b
Partial
Agg
Partial
Agg
Partial
Agg
Agg
Agg
Agg
Shuffle
Arrow batches
Result

40. © 2016 Dremio Corporation @DremioHQ
RPC: future arrow based interchange
The memory
representation is sent
over the wire.
No serialization
overhead.
Scanner
projection/predicate
push down
Operator
Arrow batches
Tablet
Mem
Disk
SQL
execution
Scanner Operator
Scanner Operator
Tablet
Mem
Disk
Tablet
Mem
Disk
…

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IPC: Python with Spark or Drill
SQL engine
Python
process
User
defined
function
SQL
Operator
1
SQL
Operator
2
reads reads

42. © 2016 Dremio Corporation @DremioHQ
What’s Next
• Parquet – Arrow conversion for Python & C++
• Arrow IPC Implementation
• Kudu – Arrow integration
• Apache {Spark, Drill} to Arrow Integration
– Faster UDFs, Storage interfaces
• Support for integration with Intel’s Persistent
Memory library via Apache Mnemonic

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Get Involved
• Join the community
– dev@{arrow,parquet,kudu.incubator}.apache.org
– Slack:
• https://apachearrowslackin.herokuapp.com/
• https://getkudu-slack.herokuapp.com/
– http://{arrow,parquet,kudu}.apache.org
– Follow @Apache{Parquet,Arrow,Kudu}