ACID support

Historically, the only way to atomically add data to a table in Hive was to add a new partition. Updating or deleting data in partition required removing the old partition and adding it back with the new data and it wasn’t possible to do atomically.

However, user’s data is continually changing and as Hive matured, users required reliability guarantees despite the churning data lake. Thus, we needed to implement ACID transactions that guarantee atomicity, consistency, isolation, and durability. Although we support ACID transactions, they are not designed to support OLTP requirements. It can support millions of rows updated per a transaction, but it can not support millions of transactions an hour.

Additionally, we wanted to support streaming ingest in to Hive tables where streaming applications like Flume or Storm could write data into Hive and have transactions commit once a minute and queries would either see all of a transaction or none of it.

HDFS is a write once file system and ORC is a write-once file format, so edits were implemented using base files and delta files where insert, update, and delete operations are recorded.

Hive tables without ACID enabled have each partition in HDFS look like:

Filename	Contents
00000_0	Bucket 0
00001_0	Bucket 1

With ACID enabled, the system will add delta directories:

Filename	Contents
00000_0	Bucket 0 base
00001_0	Bucket 1 base
delta_0000005_0000005/bucket_00000	Transaction 5 to 5, bucket 0 delta
delta_0000005_0000005/bucket_00001	Transaction 5 to 5, bucket 1 delta

When too many deltas have been created, a minor compaction will automatically run and merge a set of transactions into a single delta:

Filename	Contents
00000_0	Bucket 0 base
00001_0	Bucket 1 base
delta_0000005_0000010/bucket_00000	Transaction 5 to 10, bucket 0 delta
delta_0000005_0000010/bucket_00001	Transaction 5 to 10, bucket 1 delta

When the deltas get large enough, major compaction will re-write the base to incorporate the deltas.

Filename	Contents
base_0000010/bucket_00000	Transactions upto 10, bucket 0 base
base_0000010/bucket_00001	Transactions upto 10, bucket 1 base

Reads and compactions do not require locks and thus compactions can not destructively modify their inputs, but rather write new directories.

All rows are given an automatic assigned row id, which is the triple of original transaction id, bucket, and row id, that is guaranteed to be unique. All update and delete operations refer to that triple.

The ORC files in an ACID table are extended with several column. They are the operation (insert, update, or delete), the triple that uniquely identifies the row (originalTransaction, bucket, rowId), and the current transaction.

struct<
  operation: int,
  originalTransaction: bigInt,
  bucket: int,
  rowId: bigInt,
  currentTransaction: bigInt,
  row: struct<...>
>

The serialization for the operation codes is:

Operation	Serialization
INSERT	0
UPDATE	1
DELETE	2

When a application or query reads the ACID table, the reader provides the list of committed transactions to include. This list is produced by the Hive metastore when a query starts. The task does a merge sort. Each of the files is sorted by (originalTransaction ascending, bucket ascending, rowId ascending, and currentTransaction descending). Only the first record with a currentTransaction that is in the list of transactions to read is returned, which corresponds to the last visible update to a row.

To support streaming ingest, we add two additional features. ORC files may have additional footers written in to their body that is parsable as a complete ORC file that only includes the records already written. As the file is later extended the preliminary file footer becomes dead space within the file. Secondly, a side file named “*_flush_length” is a small file that contains a set of 8 byte values. The last complete 8 byte value is the end of the last preliminary footer.

Two properties are added to the metadata for ORC files to speed up the processing of the ACID tables. In particular, when a task is reading part of the base file for a bucket, it will use the first and last rowIds to find the corresponding spots in the delta files. The hive.acid.key.index lets the reader skip over stripes in the delta file that don’t need to be read in this task.

Key	Meaning
hive.acid.stats	Number of inserts, updates, and deletes comma separated
hive.acid.key.index	The last originalTransaction, bucket, rowId for each stripe

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