pratyakshsharma commented on a change in pull request #2967:
URL: https://github.com/apache/hudi/pull/2967#discussion_r645990277
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File path:
docs/_posts/2021-06-03-employing-right-configurations-for-hudi-cleaner.md
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+---
+title: "Employing correct configurations for Hudi's cleaner table service"
+excerpt: "Ensuring isolation between Hudi writers and readers using
`HoodieCleaner.java`"
+author: pratyakshsharma
+category: blog
+---
+
+Apache Hudi provides snapshot isolation between writers and readers. This is
made possible by Hudi’s MVCC concurrency model. In this blog, we will explain
how to employ the right configurations to manage multiple file versions.
Furthermore, we will discuss mechanisms available to users on how to maintain
just the required number of old file versions so that long running readers do
not fail.
+
+### Reclaiming space and keeping your data lake storage costs in check
+
+Hudi provides different table management services to be able to manage your
tables on the data lake. One of these services is called the **Cleaner**. As
you write more data to your table, for every batch of updates received, Hudi
can either generate a new version of the data file with updates applied to
records (COPY_ON_WRITE) or write these delta updates to a log file, avoiding
rewriting newer version of an existing file (MERGE_ON_READ). In such
situations, depending on the frequency of your updates, the number of file
versions of log files can grow indefinitely. If your use-cases do not require
keeping an infinite history of these versions, it is imperative to have a
process that reclaims older versions of the data. This is Hudi’s cleaner
service.
+
+### Problem Statement
+
+In a data lake architecture, it is a very common scenario to have readers and
writers concurrently accessing the same table. As the Hudi cleaner service
periodically reclaims older file versions, scenarios arise where a long running
query might be accessing a file version that is deemed to be reclaimed by the
cleaner. Here, we need to employ the correct configs to ensure readers (aka
queries) don’t fail.
+
+### Deeper dive into Hudi Cleaner
+
+To deal with the mentioned scenario, lets understand the different cleaning
policies that Hudi offers and the corresponding properties that need to be
configured. Options are available to schedule cleaning asynchronously or
synchronously. Before going into more details, we would like to explain a few
underlying concepts:
+
+ - **Hudi base file**: Columnar file which consists of final data after
compaction. A base file’s name follows the following naming convention:
`<fileId>_<writeToken>_<instantTime>.parquet`. In subsequent writes of this
file, file id remains the same and commit time gets updated to show the latest
version. This also implies any particular version of a record, given its
partition path, can be uniquely located using the file id and instant time.
+ - **File slice**: A file slice consists of the base file and any log files
consisting of the delta, in case of MERGE_ON_READ table type.
+ - **Hudi File Group**: Any file group in Hudi is uniquely identified by the
partition path and the file id that the files in this group have as part of
their name. A file group consists of all the file slices in a particular
partition path. Also any partition path can have multiple file groups.
+
+### Cleaning Policies
+
+Hudi cleaner currently supports below cleaning policies:
+
+ - **KEEP_LATEST_COMMITS**: This is the default policy. This is a temporal
cleaning policy that ensures the effect of having lookback into all the changes
that happened in the last X commits. Suppose a writer is ingesting data into a
Hudi dataset every 30 minutes and the longest running query can take 5 hours to
finish, then the user should retain atleast the last 10 commits. With such a
configuration, we ensure that the oldest version of a file is kept on disk for
at least 5 hours, thereby preventing the longest running query from failing at
any point in time. Incremental cleaning is also possible using this policy.
+ - **KEEP_LATEST_FILE_VERSIONS**: This policy has the effect of keeping N
number of file versions irrespective of time. This policy is useful when it is
known how many MAX versions of the file does one want to keep at any given
time. To achieve the same behaviour as before of preventing long running
queries from failing, one should do their calculations based on data patterns.
Alternatively, this policy is also useful if a user just wants to maintain 1
latest version of the file.
+
+### Examples
+
+Suppose a user is ingesting data into a hudi dataset of type COPY_ON_WRITE
every 30 minutes as shown below:
+
+
+_Figure1: Incoming records getting ingested into a hudi dataset every 30
minutes_
+
+The figure shows a particular partition on DFS where commits and corresponding
file versions are color coded. 4 different file groups are created in this
partition as depicted by fileId1, fileId2, fileId3 and fileId4. File group
corresponding to fileId2 has records ingested from all the 5 commits, while the
group corresponding to fileId4 has records from the latest 2 commits only.
Review comment:
@nsivabalan I have mentioned here that fileIds represent file groups.
Does this solve what you are looking for?
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