alexeykudinkin commented on code in PR #6268:
URL: https://github.com/apache/hudi/pull/6268#discussion_r943775756
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website/src/pages/tech-specs.md:
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+# Apache Hudi Storage Format Specification [DRAFT]
+
+
+
+This document is a specification for the Hudi Storage Format which transforms
immutable cloud/file storage systems into transactional data lakes.
+
+## Overview
+
+Hudi Storage Format enables the following features over very large collection
of files/objects
+
+- streaming primitives like incremental merges, change stream etc
+- database primitives like tables, transactions, mutability, indexes and query
performance optimizations
+
+Apache Hudi is an open source data lake platform that is built on top of the
Hudi Storage Format and it unlocks the following features
+
+- **Unified Computation model** - an unified way to combine large batch style
operations and frequent near real time streaming operations over a single
unified dataset
+- **Self-Optimized Storage** - Automatically handle all the table storage
maintenance such as compaction, clustering, vacuuming asynchronously and
non-blocking to actual data changes
+- **Cloud Native Database** - abstracts Table/Schema from actual storage and
ensures up-to-date metadata and indexes unlocking multi-fold read and write
performance optimizations
+- **Engine neutrality** - designed to be neutral and not having a preferred
computation engine. Apache Hudi will manage metadata, provide common
abstractions and pluggable interfaces to most/all common computational engines.
+
+
+
+## Storage Format
+
+### Layout Hierarchy
+
+At a high level, Hudi organizes data into a high level directory structure
under the base path (root directory for the Hudi table). The directory
structure is based on coarse-grained partitioning values set for the dataset.
Non-partitioned data sets store all the data files under the base path. Hudi
storage format has a special reserved *.hoodie* directory under the base path
that is used to store transaction logs and metadata.
+
+```
+/data/hudi_trips/ <== BASE PATH
+├── .hoodie/ <== META BASE PATH
+│ └── metadata/
+├── americas/
+│ ├── brazil/
+│ │ └── sao_paulo/ <== PARTITIONED DIRECTORY
+│ │ ├── <data_files>
+│ └── united_states/
+│ └── san_francisco/
+│ ├── <data_files>
+└── asia/
+ └── india/
+ └── chennai/
+ ├── <data_files>
+```
+
+Hudi storage format offers two table types offering different trade-offs
between ingest and query performance and the data files are stored differently
based on the chosen table type.
+
+| Table Type | Trade-off
|
+| ------------- | ------------------------------------------------------------
|
+| Copy on Write | Optimized for read performance and ideal for slow changing
datasets |
+| Merge-on-read | Optimized to balance the write and read performance and
ideal for frequently changing datasets |
+
+
+
+### Data Model
+
+Within each partition, data is organized into key-value model. Every row is
uniquely identified with a row key. To write a row into Hudi dataset, each row
must specify the following user fields
+
+| User fields | Description
|
+| --------------------------- |
------------------------------------------------------------ |
+| Partitioning key [Optional] | Value of this field defines the directory
hierarchy within the table base path. This essentially provides an hierarchy
isolation for managing data and related metadata |
+| Row key(s) | Record keys uniquely identify a record/row
within each partition if partitioning is enabled |
+| Ordering field(s) | Hudi guarantees the uniqueness constraint of
row key and the conflict resolution configuration manages strategies on how to
disambiguate when multiple records with the same keys are to be merged into the
dataset. The resolution logic can be based on an ordering field or can be
custom, specific to the dataset. To ensure consistent behaviour dealing with
duplicate records, the resolution logic should be commutative and idempotent |
+
+**Hudi metadata fields**
+
+Hudi format stores the user fields along with the row merged along with
transactional metadata fields. These fields are encoded in the data-file format
and available in the table schema.
+
+| Hudi meta-fields | Description
|
+| ---------------------------- |
------------------------------------------------------------ |
+| _hoodie_commit_time [string] | Every modification to a Hudi dataset creates
an entry into the Transaction timeline. This entry is identified with the
commit time. This field matches to the commit time of the instant in the
timeline that created this record. More on how to populate this in Hudi
transactions section below. |
+| _hoodie_record_key | Unique record key identifying the row within
the partition |
+| _hoodie_partition_path | Partition path under which the data is
organized into |
+| _hoodie_file_name | The data file name this record belongs to
|
+| _hoodie_is_deleted | Tombstone field to denote the record key is
deleted |
+
+
+
+## Transaction Log (Timeline)
+
+Data consistency in Hudi is provided using Multi-version Concurrency Control
(MVCC). Every transactional action on the Hudi table creates a new entry
(instant) in the timeline. All transactional actions follows the state
transition below
+
+* **requested** - Action is requested to start on the timeline.
+* **inflight** - Action has started running and is currently in-flight
+* **completed** - Action has completed running
+
+All actions and the state transitions are registered with the timeline using
an atomic put of special meta-file inside the *.hoodie* directory. The
requirement from the underlying storage system is to support an atomic-put and
read-after-write consistency. The meta file structure is as follows
+
+```
+[Action timestamp].[Action type].[Action state]
+```
+
+* **Action timestamp** - Monotonically increasing value to denote strict
ordering of actions in the timeline. This could be provided by an external
token provider or rely on the system epoch time at millisecond granularity.
Review Comment:
The problem is that this things are unfortunately outside of control of the
implementation:
- For the first one to be upheld the cluster(s) running Hudi jobs have to
have clocks synchronized (t/h NTP for ex)
- For the second one it's mostly to exemplify what locks are actually needed
and used for (that we're not holding locks for the duration of the whole
operation, which will kill the performance, but only holding it while we
operate on the timeline)
My point here was that i think we should call these out (not necessarily
very verbosely) since these are constraints we assume on the externalities
(cluster setup, configs provided) that we can't control
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