You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Burak Yavuz (Jira)" <ji...@apache.org> on 2019/12/23 17:21:00 UTC
[jira] [Updated] (SPARK-30334) Add metadata around semi-structured
columns to Spark
[ https://issues.apache.org/jira/browse/SPARK-30334?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Burak Yavuz updated SPARK-30334:
--------------------------------
Description:
Semi-structured data is used widely in the data industry for reporting events in a wide variety of formats. Click events in product analytics can be stored as json. Some application logs can be in the form of delimited key=value text. Some data may be in xml.
The goal of this project is to be able to signal Spark that such a column exists. This will then enable Spark to "auto-parse" these columns on the fly. The proposal is to store this information as part of the column metadata, in the fields:
- format: The format of the semi-structured column, e.g. json, xml, avro
- options: Options for parsing these columns
Then imagine having the following data:
{code:java}
+------------+-------+--------------------+
| ts | event | raw |
+------------+-------+--------------------+
| 2019-10-12 | click | {"field":"value"} |
+------------+-------+--------------------+ {code}
SELECT raw.field FROM data
will return "value"
or the following data
{code:java}
+------------+-------+----------------------+
| ts | event | raw |
+------------+-------+----------------------+
| 2019-10-12 | click | field1=v1|field2=v2 |
+------------+-------+----------------------+ {code}
SELECT raw.field1 FROM data
will return v1.
As a first step, we will introduce the function "as_json", which accomplishes this for JSON columns.
was:
Semi-structured data is used widely in the data industry for reporting events in a wide variety of formats. Click events in product analytics can be stored as json. Some application logs can be in the form of delimited key=value text. Some data may be in xml.
The goal of this project is to be able to signal Spark that such a column exists. This will then enable Spark to "auto-parse" these columns on the fly. The proposal is to store this information as part of the column metadata, in the fields:
- format: The format of the semi-structured column, e.g. json, xml, avro
- options: Options for parsing these columns
Then imagine having the following data:
{code:java}
+------------+-------+--------------------+
| ts | event | raw |
+------------+-------+--------------------+
| 2019-10-12 | click | {"field":"value"} |
+------------+-------+--------------------+ {code}
SELECT raw.field FROM data
will return "value"
or the following data
{code:java}
+------------+-------+----------------------+
| ts | event | raw |
+------------+-------+----------------------+
| 2019-10-12 | click | field1=v1|field2=v2 |
+------------+-------+----------------------+ {code}
SELECT raw.field1 FROM data
will return v1.
> Add metadata around semi-structured columns to Spark
> ----------------------------------------------------
>
> Key: SPARK-30334
> URL: https://issues.apache.org/jira/browse/SPARK-30334
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 2.4.4
> Reporter: Burak Yavuz
> Priority: Major
>
> Semi-structured data is used widely in the data industry for reporting events in a wide variety of formats. Click events in product analytics can be stored as json. Some application logs can be in the form of delimited key=value text. Some data may be in xml.
> The goal of this project is to be able to signal Spark that such a column exists. This will then enable Spark to "auto-parse" these columns on the fly. The proposal is to store this information as part of the column metadata, in the fields:
> - format: The format of the semi-structured column, e.g. json, xml, avro
> - options: Options for parsing these columns
> Then imagine having the following data:
> {code:java}
> +------------+-------+--------------------+
> | ts | event | raw |
> +------------+-------+--------------------+
> | 2019-10-12 | click | {"field":"value"} |
> +------------+-------+--------------------+ {code}
> SELECT raw.field FROM data
> will return "value"
> or the following data
> {code:java}
> +------------+-------+----------------------+
> | ts | event | raw |
> +------------+-------+----------------------+
> | 2019-10-12 | click | field1=v1|field2=v2 |
> +------------+-------+----------------------+ {code}
> SELECT raw.field1 FROM data
> will return v1.
>
> As a first step, we will introduce the function "as_json", which accomplishes this for JSON columns.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org