Hi,
thanks...but I need to sort things out with ONE SerDe/strategy...
I've started with André's idea by using Roberto Congiu's SerDe and André's
template to create a table with the right schema and loading the data
aftrerwards.
But it's not completely working...
I did the following (sorry for spaming...):
1. create table and load data
-- create database (if not exists)
CREATE DATABASE IF NOT EXISTS mdmp_api_dump;
-- connect to database;
USE mdmp_api_dump;
-- add SerDE for json processing
ADD JAR /home/hadoop/lib/hive/json-serde-1.1.4-jar-with-dependencies.jar;
-- drop old raw data
DROP TABLE IF EXISTS mdmp_raw_data;
-- create raw data table
CREATE TABLE mdmp_raw_data (
action string,
batch array<
struct<
timestamp:string,
traits:map<string,string>,
requestId:string,
sessionId:string,
event:string,
userId:string,
action:string,
context:map<string,string>,
properties:map<string,string>
>
>,
context struct<
build:map<string,string>,
device:struct<
brand:string,
manufacturer:string,
model:string,
release:string,
sdk:int
>,
display:struct<
density:double,
height:int,
width:int
>,
integrations:map<string,boolean>,
library:string,
libraryVersion:string,
locale:map<string,string>,
location:map<string,string>,
telephony:map<string,string>,
wifi:map<string,boolean>
>,
received_at string,
requestTimestamp string,
writeKey string
)
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
STORED AS TEXTFILE;
-- load data
LOAD DATA INPATH 'hdfs:///input-api/1403181319.json' OVERWRITE INTO TABLE
`mdmp_raw_data`;
2. run query against the "raw data" and create "formatted table":
ADD JAR /home/hadoop/lib/hive/json-serde-1.1.4-jar-with-dependencies.jar;
USE mdmp_api_dump;
DROP TABLE IF EXISTS mdmp_api_data;
CREATE TABLE mdmp_api_data AS
SELECT DISTINCT
a.action,
a.received_at,
a.requestTimestamp,
a.writeKey,
a.context.device.brand as brand,
a.context.device.manufacturer as manufacturer,
a.context.device.model as model,
a.context.device.release as release,
a.context.device.sdk as sdk,
-- a.context.display.density as density,
a.context.display.height as height,
a.context.display.width as width,
a.context.telephony['radio'] as tel_radio,
a.context.telephony['carrier'] as tel_carrier,
a.context.wifi['connected'] as wifi_connected,
a.context.wifi['available'] as wifi_available,
a.context.locale['carrier'] as loce_carrier,
a.context.locale['language'] as loce_language,
a.context.locale['country'] as loce_country,
a.context.integrations['Tapstream'] as int_tapstream,
a.context.integrations['Amplitude'] as int_amplitude,
a.context.integrations['Localytics'] as int_localytics,
a.context.integrations['Flurry'] as int_flurry,
a.context.integrations['Countly'] as int_countly,
a.context.integrations['Quantcast'] as int_quantcast,
a.context.integrations['Crittercism'] as int_crittercism,
a.context.integrations['Google Analytics'] as int_googleanalytics,
a.context.integrations['Mixpanel'] as int_mixpanel,
b.batch.action AS b_action,
b.batch.context,
b.batch.event,
b.batch.properties,
b.batch.requestId,
b.batch.sessionId,
b.batch.timestamp,
b.batch.traits,
b.batch.userId
FROM mdmp_raw_data a
LATERAL VIEW explode(a.batch) b AS batch;
So far so good... (besides a silly double/int bug in the outdated SerDe)
I thought.
But it turned out, that some fields are NULL - within all records.
Affected fields are:
b.batch.event,
b.batch.requestId,
b.batch.sessionId,
b.batch.userId
I can see values in the json file, but neither in the "raw table" nor in
the final table...that's really strange.
An example record:
{"requestTimestamp":"2014-06-19T14:25:26+02:00","context":{"libraryVersion":"0.6.13","telephony":{"radio":"gsm","carrier":"o2
-
de"},"wifi":{"connected":true,"available":true},"location":{},"locale":{"carrier":"o2
-
de","language":"Deutsch","country":"Deutschland"},"library":"analytics-android","device":{"brand":"htc","model":"HTC
One
S","sdk":16,"release":"4.1.1","manufacturer":"HTC"},"display":{"density":1.5,"width":540,"height":960},"build":{"name":"1.0","code":1},"integrations":{"Tapstream":false,"Amplitude":false,"Localytics":false,"Flurry":false,"Countly":false,"Bugsnag":false,"Quantcast":false,"Crittercism":false,"Google
Analytics":false,"Mixpanel":false}},"batch":[{"timestamp":"2014-06-19T14:25:17+02:00","requestId":"32377337-3f99-4ac5-bfc6-d3654584655b","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Comments","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"comment":"Ruff
ruff!"}},{"timestamp":"2014-06-19T14:25:18+02:00","requestId":"fbfd45c9-cf0f-4cb3-955c-85c65220a5bd","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Purchase","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"revenue":"0,08"}},{"timestamp":"2014-06-19T14:25:18+02:00","requestId":"3a643b12-64e5-4a7c-b44b-e3e09dbc5b66","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Comments","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"comment":"Wow..."}},{"action":"identify","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"timestamp":"2014-06-19T14:25:19+02:00","traits":{"email":"
do...@mdmp.com","name":"Carmelo
Doge"},"requestId":"ef2910f4-cd4f-4175-89d0-7d91b35c229f","sessionId":"75cd18db8a364c2","userId":"doge74167705ruffruff"},{"timestamp":"2014-06-19T14:25:19+02:00","requestId":"1676bb06-abee-4135-a206-d57c4a1bc24d","sessionId":"75cd18db8a364c2","event":"TEST
Doge App
Usage","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{}},{"timestamp":"2014-06-19T14:25:20+02:00","requestId":"66532c8a-c5da-4852-b8b6-04df8f3052d5","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Comments","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"comment":"Many
data."}},{"timestamp":"2014-06-19T14:25:21+02:00","requestId":"a1a79d8c-fe58-4567-8dec-a8d1d2ae2713","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Purchase","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"revenue":"0,87"}},{"timestamp":"2014-06-19T14:25:21+02:00","requestId":"259209ac-b135-4d5f-bdac-535eccc0400e","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Comments","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"comment":"Wow..."}},{"timestamp":"2014-06-19T14:25:23+02:00","requestId":"59b6d57c-c7a5-4b2a-af6d-fa10ae0de60c","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Comments","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"comment":"Such
App!"}},{"timestamp":"2014-06-19T14:25:24+02:00","requestId":"8b05226f-bdf5-4af8-bb91-84da1b874c6e","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Purchase","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"revenue":"0,50"}},{"timestamp":"2014-06-19T14:25:24+02:00","requestId":"0f366675-5641-4238-b2a9-176735de6edd","sessionId":"75cd18db8a364c2","event":"TEST
Doge
Comments","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"comment":"Ruff
ruff!"}},{"timestamp":"2014-06-19T14:25:26+02:00","requestId":"9e832534-5114-4ec1-bc20-1dcf1c354d0c","sessionId":"75cd18db8a364c2","event":"Session
end","userId":"doge74167705ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"start":"14:25:09","end":"14:25:26"}}],"writeKey":"a8RCFSAVjmT5qyxLKMzt12kcXWOIusvw","action":"import","received_at":"2014-06-19T12:25:29.790+00:00"}
Funny thing is, that I'm sure that I've seen these values earlier
today...I've reloaded the data/tables several times to see if this is still
working...well. :)
I'm gonna stop for today...another try tomorrow.
Thanks so far and many greetings from Berlin,
Chris
On Mon, Jun 23, 2014 at 6:57 PM, Sachin Goyal <sgo...@walmartlabs.com>
wrote:
>
> You can also use hive-json-schema to automate Hive schema generation from
> JSON:
> https://github.com/quux00/hive-json-schema
>
>
> From: Nitin Pawar <nitinpawar...@gmail.com<mailto:nitinpawar...@gmail.com
> >>
> Reply-To: "user@hive.apache.org<mailto:user@hive.apache.org>" <
> user@hive.apache.org<mailto:user@hive.apache.org>>
> Date: Monday, June 23, 2014 at 2:25 AM
> To: "user@hive.apache.org<mailto:user@hive.apache.org>" <
> user@hive.apache.org<mailto:user@hive.apache.org>>
> Subject: Re: how to load json with nested array into hive?
>
> I think you can just take a look at jsonserde
>
> It does take care of nested json documents. (though you will need to know
> entire json structure upfront)
>
> Here is example of using it
> http://blog.cloudera.com/blog/2012/12/how-to-use-a-serde-in-apache-hive/
>
>
>
>
> On Mon, Jun 23, 2014 at 2:28 PM, Christian Link <christian.l...@mdmp.com
> <mailto:christian.l...@mdmp.com>> wrote:
> Hi Jerome,
>
> thanks...I've already found "Brickhouse" and the Hive UDFs, but it didn't
> help.
>
> Today I'll try again to process the json file after going through all my
> mails...maybe I'll find a solution.
>
> Best,
> Chris
>
>
> On Fri, Jun 20, 2014 at 7:16 PM, Jerome Banks <jba...@tagged.com<mailto:
> jba...@tagged.com>> wrote:
> Christian,
> Sorry to spam this newsgroup, and this is not a commercial endorsement,
> but check out the Hive UDFs in the Brickhouse project (
> http://github.com/klout/brickhouse ) (
> http://brickhouseconfessions.wordpress.com/2014/02/07/hive-and-json-made-simple/
> )
>
> You can convert arbitrary complex Hive structures to an from json with
> it's to_json and from_json UDF's. See the blog posting for an explanation.
>
> -- jerome
>
>
> On Fri, Jun 20, 2014 at 8:26 AM, Christian Link <christian.l...@mdmp.com
> <mailto:christian.l...@mdmp.com>> wrote:
> hi,
>
> I'm very, very new to Hadoop, Hive, etc. and I have to import data into
> hive tables.
>
> Environment: Amazon EMR, S3, etc.
>
> The input file is on S3 and I copied it into my HDFS.
>
> 1. flat table with one column and loaded data into it:
>
> CREATE TABLE mdmp_raw_data (json_record STRING);
> LOAD DATA INPATH 'hdfs:///input-api/1403181319.json' OVERWRITE INTO
> TABLE `mdmp_raw_data`;
> That worked, I can access some data, like this:
>
> SELECT d.carrier, d.language, d.country
> FROM mdmp_raw_data a LATERAL VIEW json_tuple(a.data, 'requestTimestamp',
> 'context') b AS requestTimestamp, context
> LATERAL VIEW json_tuple(b.context, 'locale') c AS locale
> LATERAL VIEW json_tuple(c.locale, 'carrier', 'language', 'country') d AS
> carrier, language, country
> LIMIT 1;
>
> Result: o2 - de Deutsch Deutschland
>
> I can also select the array at once:
>
> SELECT b.requestTimestamp, b.batch
> FROM mdmp_raw_data a
> LATERAL VIEW json_tuple(a.data, 'requestTimestamp', 'batch') b AS
> requestTimestamp, batch
> LIMIT 1;
> This will give me:
>
>
> [{"timestamp":"2014-06-19T14:25:18+02:00","requestId":"2ca08247-5542-4cb4-be7e-4a8574fb77a8","sessionId":"f29ec175ca6b7d10","event":"TEST
> Doge
> Comments","userId":"doge96514016ruffruff","action":"track","context":{"library":"analytics-android","libraryVersion":"0.6.13"},"properties":{"comment":"Much
> joy."}}, ...]
>
> This "batch" may contain n events will a structure like above.
>
> I want to put all events in a table where each "element" will be stored in
> a unique column: timestamp, requestId, sessionId, event, userId, action,
> context, properties
>
> 2. explode the "batch" I read a lot about SerDe, etc. - but I don't get it.
>
> - I tried to create a table with an array and load the data into it -
> several errors
> use explode in query but it doesn't accept "batch" as array
> - integrated several SerDes but get things like "unknown function jspilt"
> - I'm lost in too many documents, howtos, etc. and could need some
> advices...
>
> Thank you in advance!
>
> Best, Chris
>
>
>
>
>
> --
> Nitin Pawar
>
