The document discusses strategies for managing Hive tables stored in cloud storage systems. It notes key differences between cloud storage and traditional file systems, including that cloud storage uses paths instead of directories and keys instead of users/permissions. It then outlines several approaches for micro-managing Hive tables in cloud storage to avoid issues like rename collisions and inconsistent reads. This includes using transactional properties, partitioning, and a "take a number" approach for inserts to track and isolate concurrent writes. Measurements show a 21% reduction in partition load time using these strategies.

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Hive-14535 : Cloud storage
Gopal V

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Cloud “FileSystems” are Strange Beasts
“There are no directories. Only paths.”
“There are no users. Only keys.”
“There are no permissions. Only acl rules.”
“There is consistency, but not as we know it.”

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“Directories vs Paths.”
• Storage of Path information can be assumed to be a sorted hash-table.
• File listings are no longer listing off a tree, but prefix search
• Directories don’t need to necessarily exist for a path below it
• Listing a single level is more complex than a full-depth traversal
• Renames can cause rebalancing and moving about of the structure
• Adjacent files are sometimes more expensive than random ops

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“Users & permissions vs keys & ACLs”
• Distinguishing the user for an accessing process has no meaning
• Access keys are often rotated and occasionally invalidated
• User identity can be mapped to a key (externally or by id management)
• Buckets are commonly used to differentiate stores, instead of permissions
• Permissions are rarely set or applied per-file, but across path patterns
• Permissions set to a directory need extra user checks to be useful (chmod +x)

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“Consistency”
• Arguably the most complex issue
• Renames needn’t be consistent, creates can have collisions
• Reads can return old data for the same path when overwriting
• Versioned reads are complex to manage and hard to throw a “Time machine” over
• Cross-Region Replication often lags and doubles stale-read issues

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Micro-Managed Hive Tables
• Support for all Hive input formats, including user ones
• Avoid rename operations as much as possible
• Never collide final paths for different inserts
• Ongoing inserts should be atomic across > 1 partitions
• Snapshot isolation for data reads for existing partitions being back-filled
• Stage data without accidental partial-reads for bucket replication

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Micro-Managed Hive Tables
CREATE TABLE `web_returns_hive_commit`(…
`wr_net_loss` float)
PARTITIONED BY (`wr_returned_date_sk` int)
STORED AS <FORMAT>
LOCATION 's3a://hwdev-hive-14535/web_returns_hive_commit'
TBLPROPERTIES
('transactional'='true',
'transactional_properties'='insert_only');

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Micro-Managed Hive Tables
drwxrwxrwx - cloudbreak 0 2016-12-07 21:42 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450820
drwxrwxrwx - cloudbreak 0 2016-12-07 21:42 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450820/mm_0
-rw-rw-rw- 1 cloudbreak 1791 2016-12-07 00:55 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450820/mm_0/000021_0
drwxrwxrwx - cloudbreak 0 2016-12-07 21:42 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450821
drwxrwxrwx - cloudbreak 0 2016-12-07 21:42 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450821/mm_0
-rw-rw-rw- 1 cloudbreak 2186 2016-12-07 00:55 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450821/mm_0/000022_0
drwxrwxrwx - cloudbreak 0 2016-12-07 21:42 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450822
drwxrwxrwx - cloudbreak 0 2016-12-07 21:42 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450822/mm_0
-rw-rw-rw- 1 cloudbreak 1814 2016-12-07 00:55 s3a://hwdev-hive-14535/web_returns_hive_commit/wr_returned_date_sk=2450822/mm_0/000023_0
/web_returns_hive_commit/wr_returned_date_sk=2450820/mm_0/000021_0

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“Take a number” for inserts

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Read: tracking committed data
• Similar to Hive-ACID (ORC)
• Committed txns disappear from the tracking data
• With each query, it takes a highest known txn + list of open/aborted txns
• All valid transactions are < max(transaction_id) and not IN (open_txns)
• The transaction filtering is done at the listing level for all formats

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Branch + Future Work
Current measurement has 21% reduction in partition load time (+HIVE-15368)
Time taken to load dynamic partitions: 350.846 seconds -> 274.715 seconds
Work continues in the branch for hive-14535
Work ongoing to optimize to take advantage of faster recursive listings
Discussions towards incremental refresh for cube engines for backfill
Questions?
Suggestions?