Apache Hudi Technical Specification

Hudi brings database capabilities (tables, transactions, mutability, indexes, storage layouts) along with an incremental stream processing model (incremental merges, change streams, out-of-order data) over very large collections of files/objects, turning immutable cloud/file storage systems into transactional data lakes.

Overview​

This document is a specification for the Hudi's storage format along with protocols for correctly implementing readers and writers to accomplish the following goals.

• Unified Computation Model - a unified way to combine large batch style operations and frequent near real time incremental operations over large datasets.
• Self-Optimized Storage - automatically handle all the table storage maintenance such as compaction, clustering, vacuuming asynchronously and in most cases 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.
• Cross-Engine Compatibility - designed to be neutral and compatible with different computation engines. Hudi will manage metadata, and provide common abstractions and pluggable interfaces to most/all common compute/query engines.

This document is intended as reference guide for any compute engines, that aim to write/read Hudi tables, by interacting with the storage format directly.

Storage Layout​

Hudi organizes a table as a collection of files (objects in cloud storage) that can be spread across different paths on storage which can be local filesystem, distributed filesystem or object storage. These different paths that contain a table's files are called partitions. Some common ways of organizing files can be as follows

• Hierarchical folder tree: files are organized under a folder path structure like conventional filesystems, for ease of access and navigation. Hive-style partitioning is a special case of this organization, where the folder path names indicate the field values that partition the data. However, note that, unlike Hive style partitioning, partition columns are not removed from data files and partitioning is a mere organizational construct.
• Cloud-optimized with random-prefixes: files are distributed across different paths (of even varying depth) across cloud storage, to circumvent scaling/throttling issues that plague cloud storage systems, at the cost of losing easy navigation of storage folder tree using standard UI/CLI tools.
• Unpartitioned flat structure: tables can also be totally unpartitioned, where a single folder contains all the files that constitute a table.

Metadata about the table is stored at a location on storage, referred to as basepath, which contains a special reserved .hoodie directory under the base path is used to store transaction logs, metadata and indexes. A special file hoodie.properties under basepath persists table level configurations, shared by writers and readers of the table. These configurations are explained here, and any config without a default value needs to be specified during table creation.

/data/hudi_trips/                   <-- Base Path
├── .hoodie/                        <-- Meta Path
|   └── hoodie.properties           <-- Table Configs
│   └── metadata/                   <-- Metadata
|       └── files/                  <-- Files that make up the table
|       └── col_stats/              <-- Statistics on files and columns
├── americas/                       <-- Data stored as folder tree
│   ├── brazil/
│   │   └── sao_paulo               <-- Partition Path 
│   │       ├── [data_files]
│   └── united_states/
│       └── san_francisco/
│           ├── [data_files]
└── asia/
    └── india/
        └── chennai/
            ├── [data_files]

Data files can be either base files or log files. Base files contain records stored in columnar or SST table like file formats depending on use-cases. Log files store deltas (partial or complete), deletes, change logs and other record level operations performed on the records in the base file. Data files are organized into logical concepts called file groups, uniquely identified by a file id. Each record in the table is identified by an unique key and mapped to a single file group at any given time. Within a file group, data files are further split into file slices, where each file slice contains an optional base file and a list of log files, that constitute the state of all records in the file group at a given time. These constructs allows Hudi to efficiently support incremental operations, as will be evident later.

Timeline​

Hudi stores all actions performed on a table into a Log Structured Merge (LSM) tree structure called the Timeline. Unlike typical LSM implementations, the memory component and the write-ahead-log are at once replaced by avro serialized files containing individual actions (active timeline) for high durability and inter-process co-ordination. Every action on the Hudi table creates a new entry (instant) in the active timeline and periodically, actions move from the active timeline to the LSM structure. Each new actions and state changes need to be atomically published to the timeline (see Appendix for storage specific guidelines to achieve that).

Time​

Each action on the timeline is stamped with a time at which it began and completed. The notion of time can be logical or physical timestamps, but it's required that each timestamp generated by a process is monotonically increasing with respect to the previous time generated by the same or another process. This requirement can be satisfied by implementing a TrueTime generator with an external time generator or rely on system epoch times with assumed bounds on clock skews. See appendix for more.

Actions​

Action have a plan (optional) and completion metadata associated with them that capture how the action alters the table state. The metadata is serialized as Json/Avro, and the schema for each of these actions is described in avro here. The following are the actions on the Hudi timeline.

States​

An action can be in any one of the following states on the active timeline.

All the actions in requested/inflight states are stored in the active timeline as files named [begin_instant_time].[action_type].[requested|inflight]. Completed actions are stored along with a time that denotes when the action was completed, in a file named [begin_instant_time]_[completion_instant_time].[action_type].

LSM Timeline​

Completed actions, their plans and completion metadata are stored in a more scalable LSM tree based timeline organized in an archived storage location under the .hoodie metadata path. It consists of Apache Parquet files with action instant data and bookkeeping metadata files, in the following manner.

/.hoodie/archived/ 					
├── _version_                               <-- stores the manifest version that is current
├── manifest_1                              <-- manifests store list of files in timeline
├── manifest_2                              <-- compactions, cleaning, writes produce new manifest files
├── ...                                      
├── manifest_[N]                            <-- there can be many manifest files at any given time
├── [min_time]_[max_time]_[level].parquet   <-- files storing actual action details

The schema of the individual files are as follows.

The actual parquet file schema is:

Table Types​

Hudi storage format supports two table types offering different trade-offs between write and query performance (see appendix for more details) and records are stored differently based on the chosen table type.

Readers need to then satisfy different query types on these tables.

• Snapshot queries - query the table for latest committed state of each record.
• Time-Travel queries - snapshot query performed as of a point in timeline or equivalent on an older snapshot of the table.
• Incremental queries - obtain the latest merged state of all records that have been updated/inserted between two points in the timeline.
• Change Data Capture - obtain a change log of all modifications (updates, inserts, deletes) with before/after images for each record, between two points in the timeline.

Data Files​

Data Files have naming conventions that allows to easily track history of files with respect to the timeline as well as serving practical operational needs. For e.g it's quite possible to recover the table to a known good state, when operational accidents cause the timeline and other metadata are deleted.

Base Files​

Base files are standard well-known file formats like Apache Parquet, Apache Orc and Apache HBase's HFile, named as [file_id]_[write_token]_[begin_time].[extension].

where,

• file_id - Id of the file group that the base file belong to.
• write_token - Monotonically increasing token for every attempt to write the base file within a given transaction. This should help uniquely identifying the base file when there are failures and retries. Cleaning can remove partial/uncommitted base files by comparing with the successful write token.
• begin_time - Time indicating the begin time of the action that generated this file.
• extension - base file extension matching the file format such as .parquet, .orc.

Note that a single file group can contain base files with different extensions.

Log Files​

The log files contain different type of blocks, that encode delta updates, deletes or change logs against the base file. They are named with the convention

.[file_id]_[begin_instant_time].log.[version]_[write_token].

• file_id - File Id of the base file in the slice
• begin_instant_time - Time at which the write operation that produced this log file was requested.
• version - Current version of the log file format, to order deltas against the base file.
• write_token - Monotonically increasing token for every attempt to write the log file. This should help uniquely identifying the log file when there are failures and retries. Cleaner can clean-up partial log files if the write token is not the latest in the file slice.

Log Format​

The Log file format structure is a Hudi native format. The actual content bytes are serialized into one of Apache Avro, Apache Parquet or Apache HFile file formats based on configuration and the other metadata in the block is serialized using primitive types and byte arrays.

Hudi Log format specification is as follows.

Versioning​

Log file format versioning refers to a set of feature flags associated with a log format. The current version is 1. Versions are changed when the log format changes. The feature flags determine the behavior of the log file reader. The following are the feature flags associated with the log file format.

Headers​

Metadata key mapping from Integer to actual metadata is as follows:

Block types​

The following are the possible block types used in Hudi Log Format:

Command Block (Id: 1)​

Encodes a command to the log reader. The Command block must be 0 byte content block which only populates the metadata Command Block Type. Only possible values in the current version of the log format is ROLLBACK_PREVIOUS_BLOCK, which lets the reader to undo the previous block written in the log file. This denotes that the previous action that wrote the log block was unsuccessful.

Delete Block (Id: 2)​

Corrupted Block (Id: 3)​

This block type is never written to persistent storage. While reading a log block, if the block is corrupted, then the reader gets an instance of the Corrupted Block instead of a Data block. It is a reserved ID for handling cases of corrupt or partially written blocks, for example on HDFS.

Avro Block (Id: 4)​

Data block serializes the actual records written into the log file

HFile Block (Id: 5)​

The HFile data block serializes the records using the HFile file format. HFile data model is a key value pair and both are encoded as byte arrays. Hudi record key is encoded as Avro string and the Avro record serialized using BinaryEncoder is stored as the value. HFile file format stores the records in sorted order and with index to enable quick point reads and range scans.

Parquet Block (Id: 6)​

The Parquet Block serializes the records using the Apache Parquet file format. The serialization layout is similar to the Avro block except for the byte array content encoded in columnar Parquet format. This log block type enables efficient columnar scans and better compression. Different data block types offers different trade-offs and picking the right block is based on the workload requirements and is critical for merge and read performance.

CDC Block (Id: 7)​

The CDC block is used for change data capture and it encodes the before and after image of the record as an Avro data block as follows:

Table Properties​

As mentioned in the storage layout section, the table properties are stored in the hoodie.properties file under the .hoodie directory in the table base path. Below is the list of properties that are stored in this file.

The record key, precombine and partition fields are optional but play an important role in modeling data stored in Hudi table.

Metadata​

Hudi automatically extracts the physical data statistics and stores the metadata along with the data to improve write and query performance. Hudi Metadata is an internally-managed table which organizes the table metadata under the base path .hoodie/metadata. The metadata is in itself a Hudi table, organized with the Hudi merge-on-read storage format. Every record stored in the metadata table is a Hudi record and hence has partitioning key and record key specified. Following metadata is stored in the metadata table.

• files - Partition path to file name index. Key for the Hudi record is the partition path and the actual record is a map of file name to an instance of HoodieMetadataFileInfo. Additionally, a special key __all_partitions__ holds the list of all partitions. The files index can be used to do file listing and do filter based pruning of the scanset during query
• column_stats - contains statistics of columns for all the records in the table. This enables fine-grained file pruning for filters and join conditions in the query. The actual payload is an instance of HoodieMetadataColumnStats.

Apache Hudi platform employs HFile format, to store metadata and indexes, to ensure high performance, though different implementations are free to choose their own.

Schema​

[WIP] Tracking schema versions.

File Listings​

File listing is an index of partition path to file name stored under the partition files in the metadata table. The files index can be used to do file listing and do filter based pruning of the scanset during query. Key for the Hudi record is the partition path and the actual record is a map of file name to an instance of HoodieMetadataFileInfo that encodes file group ID and instant time along with file metadata. Additionally, a special key __all_partitions__ holds the list of all partitions.

Column Statistics​

Column statistics is stored under the partition column_stats in the metadata table. The actual payload is an instance of HoodieMetadataColumnStats. It contains statistics (min/max/nullCount) of columns for all the records in the table. This enables fine-grained file pruning for filters and join conditions in the query. The Hudi key is str_concat(hash(column name), str_concat(hash(partition name), hash(file name))) and the actual payload is an instance of HoodieMetadataColumnStats.

Meta Fields​

In addition to the fields specified by the table's schema, the following meta fields are added to each record, to unlock incremental processing and ease of debugging. These meta fields are part of the table schema and

stored with the actual record to avoid re-computation.

Within a given file, all records share the same values for _hoodie_partition_path and _hoodie_file_name, thus easily compressed away without any overheads with columnar file formats. The other fields can also be optional for writers depending on whether protection against key field changes or incremental processing is desired. More on how to populate these fields in the sections below.

Indexes​

Naming​

Bloom Filter Index​

Bloom filter index is used to accelerate 'presence checks' validating whether particular record is present in the file, which is used during merging, hash-based joins, point-lookup queries, etc. The bloom filter index is stored in Hudi metadata table under the partition bloom_filters. The Hudi key is str_concat(hash(partition name), hash(file name)) and the actual payload is an instance of HudiMetadataBloomFilter with the following schema:

Record Index​

Record index contains mappings of individual record key and the corresponding file group id. The record index not only dramatically boosts write efficiency but also improves read efficiency for keyed lookups. The record index is stored in Hudi metadata table under the partition record_index. The Hudi key is the value of record key and the actual payload is an instance of HoodieRecordIndexInfo with the following schema:

Secondary Indexes​

Functional Indexes​

A functional index is an index on a function of a column. Hudi supports creating functional indexes for certain unary string and timestamp functions supported by Apache Spark. The index definition specified by the user is serialized to JSON format and saved at a path specified by hoodie.table.index.defs.path in table properties. Index itself is stored in Hudi metadata table under the partition func_index_<user_specified_index_name>.

We covered different storage layouts earlier. Functional index aggregates stats by storage partitions and, as such, partitioning can be absorbed into functional indexes. From that perspective, some useful functions that can also be applied as transforms on a field to extract and index partitions are listed below.

Full list of supported functions can be found in HoodieSparkFunctionalIndex.

Relational Model​

This section describes how to implement a traditional relational model, with concurrent writers on top of the storage format described so far. Specifically, it aims to employ optimistic concurrency control between writers to guarantee serializable execution of write operations. To achieve this, Hudi assumes the availability of a distributed lock service (see appendix for lock provider implementations) to acquire an exclusive table level lock.

There are three types of processes at play at any given time :

• Writers that are modifying the state of the table, with updates, deletes and inserts.
• Table services which do not logically change the state of the table.
• Readers that are querying the state of the table.

Writer Expectations​

Writers generate a begin time for their actions, proceed to create new base and log files and will finally transition the action state to completed atomically on the timeline as follows.

1. Writer requests a begin time for the write action as defined in the Timeline section above (this may involve a lock, depending on TrueTime generation mechanism used). Writer also records the latest completion time on the timeline (let's call this snapshot write time).
2. Writer produces new base and log files with updated, deleted, inserted records, while ensuring updates/deletes reach the correct file group by looking up an index as of the snapshot write time (see appendix for an alternative way to encode updates as deletes to an existing file group and inserts into a new file group, along with performance tradeoffs).
3. Writer then grabs a distributed lock and proceeds to perform the following steps within the critical section of the lock to finalize the write or fail/abort.
   1. Obtain metadata about all actions that have completed with completion time greater than base snapshot time. This defines the concurrent set of actions against which the writer needs to checks if any concurrent write operations or table service actions conflict with it.
   2. Writer checks for any overlapping file groups that have been written to in the concurrent set and decides to abort if so.
   3. Writer checks for any compactions planned or clustering actions completed on overlapping file groups and decides to abort if so.
   4. Writer reads out all record keys written by the concurrent set and compares it against keys from files it is about to commit. If there are any overlaps, the writer decides to abort (implementations could omit this check for performance reasons with the ensuing tradeoffs/limitations, if deemed acceptable)
   5. Writer checks
   6. If any of the checks above are true, writer releases the lock and aborts the write.
4. If checks in (3) pass, the writer proceeds to finalizing the write, while holding the lock.
   1. Writer updates the metadata and index based on the new files/records added to the table, and commits it to the metadata table timeline.
   2. Writer generates a completion time for the write and records it to the timeline atomically, along with necessary metadata
   3. Optionally, writer plans any table services or even runs them. This is again an implementation choice.
   4. Writer releases the lock.

Note that specific writer implementations can choose to even abort early to improve efficiency and reduce resource wastage, if they can detect conflicts using mechanisms like marker files described in the appendix.

Table Service/Writer Concurrency​

All table services acquire the same exclusive lock as writers above to plan and finalize action, including updating the metadata/indexes. Table services plans are serialized within the lock and once requested on the timeline are idempotent and expected to complete eventually with potential retries on failures. External table service management and built-in optional table services within writer processes can seamlessly co-exist and merely a deployment convenience.

Table services should follow these expectations to ensure they don't block each other.

Thus, Hudi does not treat table service actions as opaque modifications to the table and thus supports running compaction without blocking writers helpful for high update/concurrency workloads.

Concurrent readers​

The model supports snapshot isolation for concurrent readers. A reader constructs the state of the table based on the action with the greatest completion time and proceeds to construct file slices out of file groups that are of interest to the read, based on the type of query. Two concurrent readers are never in contention even in the presence of concurrent writes happening.

Reader Expectations​

Readers further need to determine the correct file slice (an optional base file plus an ordered list of log files) to read in order to construct the snapshot state using the following steps

1. Reader picks an instant in the timeline - latest completion time on the timeline or a specific time explicitly specified. Let's call this snapshot read time.
2. Reader computes all file groups in the table, by first all files part of the table, grouping them by file_id and then eliminating files that don't belong to any completed write action on the timeline
3. Reader then further eliminates replaced file groups, by removing any file groups that are part of any replacecommit actions completed on the timeline.
4. For each remaining file group, the reader proceeds to determine the correct file slice as follows.
   1. find the base file with greatest begin time less than snapshot read time.
   2. obtain all log files with completion time less than or equal to snapshot read time, sorted by completion time.
5. For each file slice obtained, the reader proceeds to merge the base and log files as follows.
   1. When the base file is scanned, for every record block, the reader has to lookup if there is a newer version of the record available in the log blocks and merge them into the record iterator.

Obtaining the listings from storage could be slow or inefficient. It can be further optimized by caching in memory or using the files metadata or with the support of an external timeline serving system.

Incremental Streaming Model​

Optimistic concurrency control yields poor performance with long running transactions and even moderate levels of writer contention. While the relation model is well-understood, serializability based on arbitrary system time (e.g completion times in Hudi or SCNs in databases) may be an overkill and even inadequate for many scenarios. Hudi storage also supports an alternative model based on stream processing techniques, with the following changes.

1. Event-time based ordering: Instead of merging base and log files based on completion times, latest value for a record is picked based on a user-specified event field that denotes the latest version of the record.
2. Relaxed expectations to block/abort: By tracking the span of actions using both begin and completion times on the timeline, processes can detect conflicting actions and adjust file slicing accordingly. For e.g this model allows compaction to be even planned without blocking writer processes.
3. Custom merging : Support implementation of custom merges using RecordPayload and RecordMerger APIs.

Non-blocking Concurrency Control​

[WIP] Please refer to RFC-66 for more and a proof of correctness.

Table Management​

All table services can be run synchronous with the Table client that merges modifications to the data or can be run asynchronously to the table client. Asynchronous is default mode in the Apache Hudi platform. Any client can trigger table management by registering a 'requested' state action in the Hudi timeline. Process in charge of running the table management tasks asynchronously looks for the presence of this trigger in the timeline.

Compaction​

Compaction is the process that efficiently updates a file slice (base and log files) for efficient querying. It applies all the batched up updates in the log files and writes a new file slice. The logic to apply the updates to the base file follows the same set of rules listed in the Reader expectations.

Log Compaction​

Log compaction is a minor compaction operation that stitches together log files into a single large log file, thus reducing write amplification. This process involves introducing a new action in Hudi called logcompaction, on the timeline. The log-compacted file is written to the same file group as the log files being compacted. Additionally, the log-compacted log block header contains the COMPACTED_BLOCK_TIMES and the log file reader can skip log blocks that have been compacted, thus reducing read amplification as well. See RFC-48 for more details.

Re-writing​

If the natural ingestion ordering does not match the query patterns, then data skipping does not work efficiently. It is important for query efficiency to be able to skip as much data on filter and join predicates with column level statistics. Clustering columns need to be specified on the Hudi table. The goal of the clustering table service, is to group data often accessed together and consolidate small files to the optimum target file size for the table.

1. Identify file groups that are eligible for clustering - this is chosen based on the clustering strategy (file size based, time based etc)
2. Identify clustering groups (file groups that should be clustered together) and each group should expect data sizes in multiples of the target file size.
3. Persist the clustering plan in the Hudi timeline as a replacecommit, when clustering is requested.
4. Clustering execution can then read the individual clustering groups, write back new file groups with target size with base files sorted by the specified clustering columns.

Cleaning​

Cleaning is a process to free up storage space. Apache Hudi maintains a timeline and multiple versions of the files written as file slices. It is important to specify a cleaning protocol which deletes older versions and reclaims the storage space. Cleaner cannot delete versions that are currently in use or will be required in future. Snapshot reconstruction on a commit instant which has been cleaned is not possible.

For e.g, there are a couple of retention policies supported in Apache Hudi platform

• keep_latest_commits: This is a temporal cleaning policy that ensures the effect of having look-back into all the changes that happened in the last X commits.
• keep_latest_file_versions: This policy has the effect of keeping a maximum of N number of file versions irrespective of time.

Apache Hudi provides snapshot isolation between writers and readers by managing multiple files with MVCC concurrency. These file versions provide history and enable time travel and rollbacks, but it is important to manage how much history you keep to balance your storage costs.

Indexing​

Indexing is an asynchronous process to create indexes on the table without blocking ingestion writers. The indexing is divided into two phases: scheduling and execution. During scheduling, the indexer takes a lock for a short duration and generates an indexing plan for data files based off of a snapshot. Index plan is serialized as avro bytes and stored in [instant].indexing.requested file in the timeline. Here's the schema for index plan

During execution, the indexer executes the plan, writing the index base file in the metadata table. Any ongoing commit update the index in log files under the same filegroup. After writing the base file, the indexer checks for all completed commit instants after t to ensure each of them added entries per its indexing plan, otherwise simply abort gracefully. Finally, when the indexing is complete, the indexer writes the [instant].indexing to the timeline. Indexer only takes a lock while adding events to the timeline and not while writing index files. See RFC-45 for now.

Compatibility​

Compatibility between different readers and writers is enforced through the hoodie.table.version table config. Hudi storage format evolves in a backwards compatible way for readers, where newer readers can read older table versions correctly. However older readers may be required to upgrade in order to read higher table versions. Hence, we recommend upgrading readers first, before upgrading writers when the table version evolves.

Change Log​

version 7​

[WIP] This is a breaking format version. Users may need to first migrate completely to the new timeline before resuming normal table operations.

version 6 & earlier​

Please refer to the previous tech specs document.

APIs​

RecordMerger API​

Hudi data model ensures record key uniqueness constraint, to maintain this constraint every single record merged into the table needs to be checked if the same record key already exists in the table. If it does exist, the conflict resolution strategy is applied to create a new merged record to be persisted. This check is done at the file group level and every record merged needs to be tagged to a single file group. By default, record merging is done during the merge which makes it efficient for queries but for certain real-time streaming requirements, it can also be deferred to the query as long as there is a consistent way to mapping the record key to a certain file group using consistent hashing techniques.

Indexing Functions​

[WIP] See RFC-63 for design and direction.

[WIP] Appendix​

Formal Proofs​

Timeline Maintenance​

Atomic writes to storage​

The requirement from the underlying storage system is to support an atomic-put and read-after-write consistency.

TrueTime Implementations​

Lock Provider Implementations​

Apache Hudi platform uses optimistic locking and provides a pluggable LockProvider interface and multiple implementations are available out of the box (Apache Zookeeper, DynamoDB, Apache Hive and JVM Level in-process lock). It is also worth noting that, if multiple writers originate from the same JVM client, a simple locking at the client level would serialize the writes and no external locking needs to be configured.

Balancing write and read performance​

A critical design choice for any table is to pick the right trade-offs in the data freshness and query performance spectrum. Hudi storage format lets the users decide on this trade-off by picking the table type, record merging and file sizing.

Interesting observation on the MOR table format is that, by providing a special view of the table which only serves the base files in the file slice (read optimized query of MOR table), query can pick between query efficiency and data freshness dynamically during query time. Compaction frequency determines the data freshness of the read optimized view. With this, the MOR has all the levers required to balance the merge and query performance dynamically.

​

Sizing the file group is extremely critical to balance the merge and query performance. Larger the file size, the more the write amplification when new file slices are being created. So to balance the merge cost, compaction or merge frequency should be tuned accordingly and this has an impact on the query performance or data freshness.

Logging Updates as Deletes + Inserts​

Optimistic concurrency efficiency​

The efficiency of Optimistic concurrency is inversely proportional to the possibility of a conflict, which in turn depends on the running time and the files overlapping between the concurrent writers. Apache Hudi storage format makes design choices that make it possible to configure the system to have a low possibility of conflict with regular workloads

• All records with the same record key are present in a single file group. In other words, there is a 1-1 mapping between a record key and a file group id, at all times.
• Unit of concurrency is a single file group and this file group size is configurable. If the table needs to be optimized for concurrent updates, the file group size can be smaller than default which could mean lower collision rates.
• Merge-on-read storage engine has the option to store the contents in record oriented file formats which reduces write latencies (often up to 10 times compared to columnar storage) which results in less collision with other concurrent writers
• Merge-on-read storage engine combined with scalable metadata table ensures that the system can handle frequent updates efficiently which means ingest jobs can be frequent and quick, reducing the chance of conflicts

Marker mechanism to remove uncommitted data​

See this and RFC-56 for now.