THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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If you are looking to quickly ingest data onto HDFS or cloud storage, Hudi can provide you tools to help. Also, if you have ETL/hive/spark jobs which are slow/taking up a lot of resources, Hudi can potentially help by providing an incremental approach to reading and writing data.
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More details can be found here. and also Design And Architecture
How do I choose a storage type for my workload
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- You are looking for a simple alternative, that replaces your existing parquet tables without any need for real-time data.
- Your current job is rewriting entire table/partition to deal with updates, while only a few files actually change in each partition.
- You are happy keeping things operationally simpler (no compaction etc), with the ingestion/write performance bound by the parquet file size and the number of such files affected/dirtied by updates
- Your workload is fairly well-understood and does not have sudden bursts of large amount of update or inserts to older partitions. COW absorbs all the merging cost on the writer side and thus these sudden changes can clog up your ingestion and interfere with meeting normal mode ingest latency targets.
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- Snapshot isolation and atomic write of batch of records
- Incremental pulls
- Ability to de-duplicate data
Find more here.
Is Hudi an analytical database
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When writing data into Hudi, you model the records like how you would on a key-value store - specify a key field (unique for a single partition/across dataset), a partition field (denotes partition to place key into) and preCombine/combine logic that specifies how to handle duplicates in a batch of records written. This model enables Hudi to enforce primary key constraints like you would get on a database table. See here for an example.
When querying/reading data, Hudi just presents itself as a json-like hierarchical table, everyone is used to querying using Hive/Spark/Presto over Parquet/Json/Avro.
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Yes. Generally speaking, Hudi is able to provide its functionality on any Hadoop FileSystem implementation and thus can read and write datasets on Cloud stores (Amazon S3 or Microsoft Azure or Google Cloud Storage). Over time, Hudi has also incorporated specific design aspects that make building Hudi datasets on the cloud easy, such as consistency checks for s3, Zero moves/renames involved for data files.
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At a high level, Hudi is based on MVCC design that writes data to versioned parquet/base files and log files that contain changes to the base file. All the files are stored under a partitioning scheme for the dataset, which closely resembles how Apache Hive tables are laid out on DFS. Please refer here for more details.
Using Hudi
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Typically, you obtain a set of partial updates/inserts from your source and issue write operations against a Hudi dataset. If you ingesting data from any of the standard sources like Kafka, or tailing DFS, the delta streamer tool is invaluable and provides an easy, self-managed solution to getting data written into Hudi. You can also write your own code to capture data from a custom source using the Spark datasource API and use a Hudi datasource to write into Hudi.
How is a Hudi job deployed
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if Hive Sync is enabled in the deltastreamer tool or datasource, the dataset is available in Hive as a couple of tables, that can now be read using HiveQL, Presto or SparkSQL. See here for more.
How does Hudi handle duplicate record keys in an input
When issuing an `upsert` operation on a dataset and the batch of records provided contains multiple entries for a given key, then all of them are reduced into a single final value by repeatedly calling payload class's preCombine() method . By default, we pick the record with the greatest value (determined by calling .compareTo()) giving latest-write-wins style semantics.
For an insert or bulk_insert operation, no such pre-combining is performed. Thus, if your input contains duplicates, the dataset would also contain duplicates. If you don't want duplicate records either issue an upsert or consider specifying option to de-duplicate input in either datasource or deltastreamer.
Can I implement my own logic for how input records are merged with record on storage
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Hudi provides built in support for rewriting your entire dataset into Hudi one-time using the HDFSParquetImporter tool available from the hudi-cli . You could also do this via a simple read and write of the dataset using the Spark datasource APIs. Once migrated, writes can be performed using normal means discussed here. This topic is discussed in detail here, including ways to doing partial migrations.
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Hudi configuration options covering the datasource and low level Hudi write client (which both deltastreamer & datasource internally call) are here. Invoking --help on any tool such as DeltaStreamer would print all the usage options. A lot of the options that control upsert, file sizing behavior are defined at the write client level and below is how we pass them to different options available for writing data.
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Yes. This can be performed either via the standalone Hive Sync tool or using options in deltastreamer tool or datasource.
How does the Hudi indexing work & what are its benefits?
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The Hudi cleaner process often runs right after a commit and deltacommit and goes about deleting old files that are no longer needed. If you are using the incremental pull feature, then ensure you configure the cleaner to retain sufficient amount of last commits to rewind. Another consideration is to provide sufficient time for your long running jobs to finish running. Otherwise, the cleaner could delete a file that is being or could be read by the job and will fail the job. Typically, the default configuration of 10 allows for an ingestion running every 30 mins to retain up-to 5 hours worth of data. If you run ingestion more frequently or if you want to give more running time for a query, consider increasing the value for the config : hoodie.cleaner.commits.retained
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Simplest way to run compaction on MOR dataset is to run the compaction inline, at the cost of spending more time ingesting; This could be particularly useful, in common cases where you have small amount of late arriving data trickling into older partitions. In such a scenario, you may want to just aggressively compact the last N partitions while waiting for enough logs to accumulate for older partitions. The net effect is that you have converted most of the recent data, that is more likely to be queried to optimized columnar format.
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The speed at which you can write into Hudi depends on the write operation and some trade-offs you make along the way like file sizing. Just like how databases incur overhead over direct/raw file I/O on disks, Hudi operations may have overhead from supporting database like features compared to reading/writing raw DFS files. That said, Hudi implements advanced techniques from database literature to keep these minimal. User is encouraged to have this perspective when trying to reason about Hudi performance. As the saying goes : there is no free lunch 
(not yet atleast)
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| Storage Type | Type of workload | Performance | Tips | ||||||||||
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| copy on write | bulk_insert | Should match vanilla spark writing + an additional sort to properly size files | properly size bulk insert parallelism to get right number of files. use insert if you want this auto tuned | ||||||||||
| copy on write | insert | Similar to bulk insert, except the file sizes are auto tuned requiring input to be cached into memory and custom partitioned. | Performance would be bound by how parallel you can write the ingested data. Tune this limit up, if you see that writes are happening from only a few executors. | ||||||||||
| copy on write | upsert/ de-duplicate & insert | Both of these would involve index lookup. Compared to naively using Spark (or similar framework)'s JOIN to identify the affected records, Hudi indexing is often 7-10x faster as long as you have ordered keys (discussed below) or <50% updates. Compared to naively overwriting entire partitions, Hudi write can be several magnitudes faster depending on how many files in a given partition is actually updated. For e.g, if a partition has 1000 files out of which only 100 is dirtied every ingestion run, then Hudi would only read/merge a total of 100 files and thus 10x faster than naively rewriting entire partition. | Ultimately performance would be bound by how quickly we can read and write a parquet file and that depends on the size of the parquet file, configured here . Also be sure to properly tune your bloom filters.
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| merge on read | bulk insert | Currently new data only goes to parquet files and thus performance here should be similar to copy_on_write bulk insert. This has the nice side-effect of getting data into parquet directly for query performance.
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| merge on read | insert | Similar to above. | |||||||||||
| merge on read | upsert/ de-duplicate & insert | Indexing performance would remain the same as copy-on-write, while ingest latency for updates (costliest I/O operation in copy_on_write) are sent to log files and thus with asynchronous compaction provides very very good ingest performance with low write amplification. |
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For copy-on-write, this is as simple as configuring the maximum size for a base/parquet file and the soft limit below which a file should be considered a small file. Hudi will try to add enough records to a small file at write time to get it to the configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all files < 100MB and try to get them upto 120MB.
For merge-on-read, there are few more configs to set. Specifically, you can configure the maximum log size and a factor that denotes reduction in size when data moves from avro to parquet files.
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For merge on read table, you may want to also try scheduling and running compaction jobs. You can run compaction directly using spark submit on org.apache.hudi.utilities.HoodieCompactor or by using HUDI CLI
Contributing to FAQ
A good and usable FAQ should be community-driven and crowd source questions/thoughts across everyone.
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