This document discusses strategies for handling mutable data in Hive's immutable data model. It presents several approaches including full refresh, full merge and replace, and partition-level merge and replace. The partition-level strategies allow merging incremental data updates into existing partitions in Hive tables. The document provides examples using Pig to filter, join, and load data to demonstrate performing incremental updates at the partition level. It evaluates the tradeoffs of different strategies based on data volumes and change rates.

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Mutable Data in Hive’s Immutable World
Lester Martin – Hortonworks 2015 Hadoop Summit

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Connection before Content
Lester Martin – Hortonworks Professional Services
lmartin@hortonworks.com || lester.martin@gmail.com
http://lester.website (links to blog, twitter,
github, LI, FB, etc)

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“Traditional” Hadoop Data
Time-Series Immutable (TSI) Data – Hive’s sweet spot
Going beyond web logs to more exotic data such as:
Vehicle sensors (ground, air, above/below water – space!)
Patient data (to include the atmosphere around them)
Smart phone/watch (TONS of info)
Clickstream Web
& Social
Geolocation Sensor
& Machine
Server
Logs
Unstructured
SOURCES

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Good TSI Solutions Exist
Hive partitions
•Store as much as you want
•Only read the files you need
Hive Streaming Data Ingest from Flume or Storm
Sqoop’s –-incremental mode of append
•Use appropriate –-check-column
•“Saved Job” remembering –last-value

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Use Case for an Active Archive
Evolving Domain Data – Hive likes immutable data
Need exact copy of mutating tables refreshed periodically
•Structural replica of multiple RDBMS tables
•The data in these tables are being updated
•Don’t need every change; just “as of” content
Existing Systems
ERP CRM SCM
SOURCES
eComm

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Start With a Full Refresh Strategy
The epitome of the KISS principle
•Ingest & load new data
•Drop the existing table
•Rename the newly created table
Surely not elegant, but solves the problem until the reload
takes longer than the refresh period

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Then Evolve to a Merge & Replace Strategy
Typically, deltas are…
•Small % of existing data
•Plus, some totally new records
In practice, differences in sizes
of circles is often much more
pronounced

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Requirements for Merge & Replace
An immutable unique key
•To determine if an addition or a change
•The source table’s (natural or surrogate) PK is perfect
A last-updated timestamp to find the deltas
Leverage Sqoop’s –-incremental mode of
lastmodified to identify the deltas
•Use appropriate –-check-column
•“Saved Job” remembering –last-value

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Processing Steps for Merge & Replace
See blog at http://hortonworks.com/blog/four-step-
strategy-incremental-updates-hive/, but note that merge
can be done in multiple technologies, not just Hive
Ingest – bring over the incremental
data
Reconcile – perform the merge
Compact – replace the existing data
with the newly merged content
Purge – cleanup & prepare to repeat

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Full Merge & Replace Will NOT Scale
The “elephant” eventually gets too big
and merging it with the “mouse” takes
too long!
Example: A Hive structure with 100
billion rows, but only 100,000 delta
records

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What Will? The Classic Hadoop Strategy!

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But… One Size Does NOT Fit All…
Not everything is “big” – in fact, most operational apps’
tables are NOT too big for a simple Full Refresh
Divide & Conquer requires additional per-table research
to ensure the best partitioning strategy is decided upon

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Criteria for Active Archive Partition Values
Non-nullable & immutable
Ensures sliding scale growth with new records generally
creating new partitions
Supports delta records being skewed such that the
percentage of partitions needing merge & replace
operations is relatively small
Classic value is (still) “Date
Created”

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Work on (FEW!) Partitions in Parallel

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Partition-Level Merge & Replace Steps
Generate the delta file
Create list of affected partitions
Perform merge & replace operations for affected partitions
1. Filter the delta file for the current partition
2. Load the Hive table’s current partition
3. Merge the two datasets
4. Delete the existing partition
5. Recreate the partition with the merged content

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What Does This Approach Look Like?
A Lightning-Fast Review of an Indicative Hybrid Pig-Hive Example

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One-Time: Create the Table
CREATE TABLE bogus_info(
bogus_id int,
field_one string,
field_two string,
field_three string)
PARTITIONED BY (date_created STRING)
STORED AS ORC
TBLPROPERTIES ("orc.compress"="ZLIB");

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One-Time: Get Content from the Source
11,2014-09-17,base,base,base
12,2014-09-17,base,base,base
13,2014-09-17,base,base,base
14,2014-09-18,base,base,base
15,2014-09-18,base,base,base
16,2014-09-18,base,base,base
17,2014-09-19,base,base,base
18,2014-09-19,base,base,base
19,2014-09-19,base,base,base

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One-Time: Read Content from HDFS
as_recd = LOAD '/user/fred/original.txt'
USING PigStorage(',') AS
(
bogus_id:int,
date_created:chararray,
field_one:chararray,
field_two:chararray,
field_three:chararray
);

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One-Time: Sort and Insert into Hive Table
sorted_as_recd = ORDER as_recd BY
date_created, bogus_id;
STORE sorted_as_recd INTO 'bogus_info'
USING
org.apache.hcatalog.pig.HCatStorer();

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One-Time: Verify Data are Present
hive> select * from bogus_info;
11 base base base 2014-09-17
12 base base base 2014-09-17
13 base base base 2014-09-17
14 base base base 2014-09-18
15 base base base 2014-09-18
16 base base base 2014-09-18
17 base base base 2014-09-19
18 base base base 2014-09-19
19 base base base 2014-09-19

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One-Time: Verify Partitions are Present
hdfs dfs -ls /apps/hive/warehouse/bogus_info
Found 3 items
…
/apps/hive/warehouse/bogus_info/date_created
=2014-09-17
…
/apps/hive/warehouse/bogus_info/date_created
=2014-09-18
…
/apps/hive/warehouse/bogus_info/date_created
=2014-09-19

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Generate the Delta File
20,2014-09-20,base,base,base
21,2014-09-20,base,base,base
22,2014-09-20,base,base,base
12,2014-09-17,base,CHANGED,base
14,2014-09-18,base,CHANGED,base
16,2014-09-18,base,CHANGED,base

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Read Delta File from HDFS
delta_recd = LOAD '/user/fred/delta1.txt'
USING PigStorage(',') AS
(
bogus_id:int,
date_created:chararray,
field_one:chararray,
field_two:chararray,
field_three:chararray
);

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Create List of Affected Partitions
by_grp = GROUP delta_recd BY date_created;
part_names = FOREACH by_grp GENERATE group;
srtd_part_names = ORDER part_names BY group;
STORE srtd_part_names INTO
'/user/fred/affected_partitions’;

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Loop/Multithread Through Affected Partitions
Pig doesn’t really help you with this problem
This indicative example could be implemented as:
•A simple script that loops through the partitions
•A Java program that multi-threads the partition-aligned processing
Multiple “Control Structures” options exist as described at
http://pig.apache.org/docs/r0.14.0/cont.html

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Loop Step: Filter on the Current Partition
delta_recd = LOAD '/user/fred/delta1.txt'
USING PigStorage(',') AS
( bogus_id:int, date_created:chararray,
field_one:chararray,
field_two:chararray,
field_three:chararray );
deltaP = FILTER delta_recd BY date_created
== '$partition_key’;

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Loop Step: Retrieve Hive’s Current Partition
all_bogus_info = LOAD 'bogus_info' USING
org.apache.hcatalog.pig.HCatLoader();
tblP = FILTER all_bogus_info
BY date_created == '$partition_key';

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Loop Step: Merge the Datasets
partJ = JOIN tblP BY bogus_id FULL OUTER,
deltaP BY bogus_id;
combined_part = FOREACH partJ GENERATE
((deltaP::bogus_id is not null) ?
deltaP::bogus_id: tblP::bogus_id) as
bogus_id, /* do for all fields
and end with “;” */

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Loop Step: Sort and Save the Merged Data
s_combined_part = ORDER combined_part BY
date_created, bogus_id;
STORE s_combined_part INTO '/user/fred/
temp_$partition_key’ USING PigStorage(',');
hdfs dfs –cat temp_2014-09-17/part-r-00000
11,2014-09-17,base,base,base
12,2014-09-17,base,CHANGED,base
13,2014-09-17,base,base,base

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Loop Step: Delete the Partition
ALTER TABLE bogus_info DROP IF EXISTS
PARTITION (date_created='2014-09-17’);

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Loop Step: Recreate the Partition
2load = LOAD '/user/fred/
temp_$partition_key'
USING PigStorage(',') AS
( bogus_id:int, date_created:chararray,
field_one:chararray,
field_two:chararray,
field_three:chararray );
STORE 2load INTO 'bogus_info' using
org.apache.hcatalog.pig.HCatStorer();

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Verify the Loop Step Updates
select * from bogus_info
where date_created = '2014-09-17’;
11 base base base 2014-09-17
12 base CHANGED base 2014-09-17
13 base base base 2014-09-17

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My Head Hurts, Too!
As Promised, We Flew Through That – Take Another Look Later

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What Does Merge & Replace Miss?
If critical, you have options
•Create a delete table sourced by a trigger
•At some wide frequency, start all over with a Full Refresh
Fortunately, ~most~ enterprises
don’t delete anything
Marking items “inactive” is
popular

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Hybrid: Partition-Level Refresh
If most of the partition is modified, just replace it entirely
Especially if the changes are only recent (or highly skewed)
Use a configured number of partitions to refresh and
assume the rest of the data is static

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Active Archive Strategy Review
strategy # of rows % of
chg
chg
skew
handles
deletes
complexity
Full Refresh <= millions any any yes simple
Full Merge &
Replace
<= millions any any no moderate
Partition-Level
Merge & Replace
billions + < 5% < 5% no complex
Partition-Level
Refresh
billions + < 5% < 5% yes complex

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Isn’t There Anything Easier?
HIVE-5317 brought us Insert, Update & Delete
•Alan Gates presented Monday
•More tightly-coupled w/o the same “hazard windows”
•“Driver” logic shifts to be delta-only & row-focused
Thoughts & attempts at true DB replication
•Some COTS solutions have been tried
•Ideally, an open-source alternative is best such as enhancing the
Streaming Data Ingest framework

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Considerations for HIVE-5317
On performance & scalability; your mileage may vary
Does NOT make Hive a RDBMS
Available in Hive .14 onwards
DDL requirements
•Must utilize partitioning & bucketing
•Initially, only supports ORC

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Recommendations
Take another look at this topic once back at “your desk”
As with all things Hadoop…
•Know your data & workloads
•Try several approaches & evaluate results in earnest
•Stick with the KISS principle whenever possible
Share your findings via blogs and local user groups
Expect (even more!) great things from Hive

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Questions?
Lester Martin – Hortonworks Professional Services
lmartin@hortonworks.com || lester.martin@gmail.com
http://lester.website (links to blog, twitter, github, LI, FB, etc)
THANKS FOR YOUR TIME!!