# About Hive-TsFile-Connector
Hive-TsFile-Connector implements the support of Hive for external data sources of Tsfile type. This enables users to operate TsFile by Hive.
With this connector, you can
- Load a single TsFile, from either the local file system or hdfs, into hive
- Load all files in a specific directory, from either the local file system or hdfs, into hive
- Query the tsfile through HQL.
- As of now, the write operation is not supported in hive-connector. So, insert operation in HQL is not allowed while operating tsfile through hive.
# System Requirements
|Hadoop Version||Hive Version||Java Version||TsFile|
| || || |
Note: For more information about how to download and use TsFile, please see the following link: https://github.com/apache/iotdb/tree/master/tsfile.
# Data Type Correspondence
|TsFile data type||Hive field type|
# Add Dependency For Hive
To use hive-connector in hive, we should add the hive-connector jar into hive.
After downloading the code of iotdb from https://github.com/apache/iotdb (opens new window), you can use the command of
mvn clean package -pl hive-connector -am -Dmaven.test.skip=true -P get-jar-with-dependencies to get a
Then in hive, use the command of
add jar XXX to add the dependency. For example:
hive> add jar /Users/hive/iotdb/hive-connector/target/hive-connector-0.10.0-jar-with-dependencies.jar; Added [/Users/hive/iotdb/hive-connector/target/hive-connector-0.10.0-jar-with-dependencies.jar] to class path Added resources: [/Users/hive/iotdb/hive-connector/target/hive-connector-0.10.0-jar-with-dependencies.jar]
# Create Tsfile-backed Hive tables
To create a Tsfile-backed table, specify the
Also provide a schema which only contains two fields:
sensor_id for the table.
time_stamp is the time value of the time series
sensor_id is the sensor name to extract from the tsfile to hive such as
The name of the table can be any valid table names in hive.
Also a location provided for hive-connector to pull the most current data for the table.
The location should be a specific directory on your local file system or HDFS to set up Hadoop.
If it is in your local file system, the location should look like
Last, set the
TBLPROPERTIES to the device name you want to analyze.
CREATE EXTERNAL TABLE IF NOT EXISTS only_sensor_1( time_stamp TIMESTAMP, sensor_1 BIGINT) ROW FORMAT SERDE 'org.apache.iotdb.hive.TsFileSerDe' STORED AS INPUTFORMAT 'org.apache.iotdb.hive.TSFHiveInputFormat' OUTPUTFORMAT 'org.apache.iotdb.hive.TSFHiveOutputFormat' LOCATION '/data/data/sequence/root.baic2.WWS.leftfrontdoor/' TBLPROPERTIES ('device_id'='root.baic2.WWS.leftfrontdoor.plc1');
In this example, the data of
root.baic2.WWS.leftfrontdoor.plc1.sensor_1 is pulled from the directory of
This table results in a description as below:
hive> describe only_sensor_1; OK time_stamp timestamp from deserializer sensor_1 bigint from deserializer Time taken: 0.053 seconds, Fetched: 2 row(s)
At this point, the Tsfile-backed table can be worked with in Hive like any other table.
# Query from TsFile-backed Hive tables
Before we do any queries, we should set the
hive.input.format in hive by executing the following command.
hive> set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
Now, we already have an external table named
only_sensor_1 in hive.
We can use any query operations through HQL to analyse it.
# Select Clause Example
hive> select * from only_sensor_1 limit 10; OK 1 1000000 2 1000001 3 1000002 4 1000003 5 1000004 6 1000005 7 1000006 8 1000007 9 1000008 10 1000009 Time taken: 1.464 seconds, Fetched: 10 row(s)
# Aggregate Clause Example
hive> select count(*) from only_sensor_1; WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases. Query ID = jackietien_20191016202416_d1e3e233-d367-4453-b39a-2aac9327a3b6 Total jobs = 1 Launching Job 1 out of 1 Number of reduce tasks determined at compile time: 1 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer=<number> In order to limit the maximum number of reducers: set hive.exec.reducers.max=<number> In order to set a constant number of reducers: set mapreduce.job.reduces=<number> Job running in-process (local Hadoop) 2019-10-16 20:24:18,305 Stage-1 map = 0%, reduce = 0% 2019-10-16 20:24:27,443 Stage-1 map = 100%, reduce = 100% Ended Job = job_local867757288_0002 MapReduce Jobs Launched: Stage-Stage-1: HDFS Read: 0 HDFS Write: 0 SUCCESS Total MapReduce CPU Time Spent: 0 msec OK 1000000 Time taken: 11.334 seconds, Fetched: 1 row(s)
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