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#Deep web iceberg diagram how to#
#Deep web iceberg diagram update#
#Deep web iceberg diagram code#

But one new parquet file is generated.(3, c_updated)

In data folder, the existing parquet files are not changed.

#Deep web iceberg diagram update#

Now, let’s use update statement to update one record. We can use spark api to read raw parquet data files, and we can find there’s one record in each parquet file. Then use the avro tools jar to read the manifest file which contains the path of the data files and other related meta info. And we find that it stores the location of manifest file and other meta info like added_data_files_count, deleted_data_files_count and etc. We can use the avro tools jar to read the manifest list file which is avro format. This manifest list file points to one manifest file which points to the 3 parquet files.

In metadata folder, the content ofversion-hint.text is changed to 2, v2.metadata.jsonis created which has one newly created snapshot which point to one manifest list file.

In data folder, 3 parquet files are created.

Then use select statement to verify the result.Īctually there’re 2 things happened underneath for this insert operation. Now let’s insert 3 new records (1, a), (2, b), (3, c) For now, this table has no data, so there’s no snapshots in this metadata file. This file contains the metadata of this table, such as the schema, location, snapshots and etc. This file only contains one number which point to the current metadata file v.medata.json) There’s only one metadata folder under the table folder (/tmp/warehouse/db/events ). Since this is a newly created table, no data is in this table.

Add a metadata folder which contains all the metadata info.

Create a directory events under the warehouse folder /tmp/warehouse.

So what does Iceberg do underneath for this create sql statement? Actually, Iceberg did 2 things： Then describe this table to check its details Now let’s start to use Spark and play Iceberg in Zeppelin.įirst Let’s create an Iceberg table events with 2 fields: idand data. Besides that, I specify the warehouse folder .warehouse explicitly so that I can check the table folder easily later in this tutorial. Here I configure the Spark interpreter as described in this quick start. %nf is a special interpreter to configure Spark interpreter in Zeppelin. Jq is used for display json, avro tools jar is used to read iceberg metadata files (avro format) and display it in plain text.

S3 means the version after we delete one record.S2 means the version after we update one record.S1 means the version after we insert 3 records.The diagram above is the architecture of Iceberg and also demonstrates what we did in this tutorial A subset of these data files compose one version of snapshot. row-count, lower-bound, upper-bound and etc.ĭata layer is a bunch of parquet files which contain all the historical data, including newly added records, updated record and deleted records. Besides that it also contains other meta info for potential optimization, e.g. It contains a collection of data files which store the table data. Manifest file can be shared cross snapshot files. Manifest list file contains a collection of manifest files. Each version of snapshot has one manifest list file. Each snapshot is associated with one manifest list file. Each CRUD operation will generate a new metadata file which contains all the metadata info of table, including the schema of table, all the historical snapshots until now and etc. In metadata layer, there’re 3 kinds of files: ( version-hint.text is the pointer which point to each version’s metadata file v.metadata.jsonin the below examples) It uses files to store where’s the current version’s metadata file. Path based catalog which is based on file system.Hive metastore uses relational database to store where’s current version’s snapshot file. Hive catalog which uses hive metastore.

#Deep web iceberg diagram code#

Then open in browser, and open the notebook Spark/Deep Dive into Iceberg which contains all the code in this article.īasically, there’re 3 layers for Iceberg: $ is the Spark folder you downloaded in Step 2.Įnter fullscreen mode Exit fullscreen mode Run the following command to start the Zeppelin docker container. Here I just summarize it as following steps:

#Deep web iceberg diagram how to#

You can check this article for how to play Spark in Zeppelin docker. You can reproduce what I did easily via Zeppelin docker. To demonstrate the internal mechanism more intuitively, I use Apache Zeppelin to run all the example code. In this post, I will use Spark sql to create/insert/delete/update Iceberg table in Apache Zeppelin and will explain what happens underneath for each operation. This post is a little different, it is for those people who are curious to know the internal mechanism of Iceberg. There’re a lot of tutorials on the internet about how to use Iceberg. Apache Iceberg is a high-performance format for huge analytic tables.