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Posted to dev@hive.apache.org by "slim bouguerra (JIRA)" <ji...@apache.org> on 2018/08/13 16:38:00 UTC
[jira] [Created] (HIVE-20377) Hive Kafka Storage Handler
slim bouguerra created HIVE-20377:
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Summary: Hive Kafka Storage Handler
Key: HIVE-20377
URL: https://issues.apache.org/jira/browse/HIVE-20377
Project: Hive
Issue Type: Bug
Affects Versions: 4.0.0
Reporter: slim bouguerra
Assignee: slim bouguerra
h1. Goal
* Read streaming data form Kafka queue as an external table.
* Allow streaming navigation by pushing down filters on Kafka record partition id, offset and timestamp.
* Insert streaming data form Kafka to an actual Hive internal table, using CTAS statement.
h1. Example
h2. Create the external table
{code}
CREATE EXTERNAL TABLE kafka_table (`timestamp` timestamps, page string, `user` string, language string, added int, deleted int, flags string,comment string, namespace string)
STORED BY 'org.apache.hadoop.hive.kafka.KafkaStorageHandler'
TBLPROPERTIES
("kafka.topic" = "wikipedia",
"kafka.bootstrap.servers"="brokeraddress:9092",
"kafka.serde.class"="org.apache.hadoop.hive.serde2.JsonSerDe");
{code}
h2. Kafka Metadata
In order to keep track of Kafka records the storage handler will add automatically the Kafka row metadata eg partition id, record offset and record timestamp.
{code}
DESCRIBE EXTENDED kafka_table
timestamp timestamp from deserializer
page string from deserializer
user string from deserializer
language string from deserializer
country string from deserializer
continent string from deserializer
namespace string from deserializer
newpage boolean from deserializer
unpatrolled boolean from deserializer
anonymous boolean from deserializer
robot boolean from deserializer
added int from deserializer
deleted int from deserializer
delta bigint from deserializer
__partition int from deserializer
__offset bigint from deserializer
__timestamp bigint from deserializer
{code}
h2. Filter push down.
Newer Kafka consumers 0.11.0 and higher allow seeking on the stream based on a given offset. The proposed storage handler will be able to leverage such API by pushing down filters over metadata columns, namely __partition (int), __offset(long) and __timestamp(long)
For instance Query like
{code}
select `__offset` from kafka_table where (`__offset` < 10 and `__offset`>3 and `__partition` = 0) or (`__partition` = 0 and `__offset` < 105 and `__offset` > 99) or (`__offset` = 109);
{code}
Will result on a scan of partition 0 only then read only records between offset 4 and 109.
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