ok - same results...so at least this seems to be stable ;)
I try to find a clever way to get the values of "integrations".
On Tue, Jun 24, 2014 at 12:37 PM, Christian Link <christian.l...@mdmp.com>
wrote:
> Nope!
> Still not working...new error
>
> Caused by: java.lang.ClassCastException:
> org.apache.hadoop.io.DoubleWritable cannot be cast to
> org.apache.hadoop.hive.serde2.io.DoubleWritable
>
> Caused by query:
> SELECT DISTINCT
> 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
> FROM mdmp_raw_data a
> LATERAL VIEW explode(a.batch) b AS batch
> WHERE a.requestTimestamp = '2014-06-19T14:08:15+02:00'
> AND a.received_at = '2014-06-19T12:08:18.734+00:00';
>
> Table structure
>
> 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>
> > from deserializer
>
> It's "density" which is causing the problem.
> When I leave out density of the query, it's running fine.
>
>
> Also, the values in "intergrations" are NULL...which had been NOT NULL,
> yesterday...strange.
>
> table mdmp_raw_data (data imported using 1.1.9.2 Serde):
>
> SELECT DISTINCT
> a.context.integrations
> FROM mdmp_raw_data a
> LATERAL VIEW explode(a.batch) b AS batch
> WHERE a.requestTimestamp = '2014-06-19T14:08:15+02:00'
> AND a.received_at = '2014-06-19T12:08:18.734+00:00';
>
>
> {"tapstream":false,"amplitude":false,"flurry":false,"quantcast":false,"countly":false,"mixpanel":false,"crittercism":false,"google
> analytics":false,"bugsnag":false,"localytics":false}
>
> json file:
> "integrations":{"Tapstream":false,"Amplitude":false,"Localytics":false,"Flurry":false,"Countly":false,"Bugsnag":false,"Quantcast":false,"Crittercism":false,"Google
> Analytics":false,"Mixpanel":false}}
>
> So they keys had been changed to LOWER CASE...hum, not sure if this makes
> sense...and WHY it happend.
>
> Again...I will import the data again and I will run the same
> queries...let's see what will change next.
>
> THANKS a lot for all the support.
>
> Best,
> Chris
>
>
>
>
> On Tue, Jun 24, 2014 at 12:11 PM, Christian Link <christian.l...@mdmp.com>
> wrote:
>
>> I've got 1.1.9.2 (thanks to Roberto) and the data is looking better...
>> I'll test the "doubel/int" thingie, now.
>>
>> Best,
>> Chris
>>
>>
>> On Mon, Jun 23, 2014 at 8:51 PM, Swagatika Tripathy <
>> swagatikat...@gmail.com> wrote:
>>
>>> Hi,
>>> Use 1.9.3 Jason serde with dependencies jar. Its the latest one I
>>> suppose.
>>>
>>> Thanks
>>> Swagatika
>>> On Jun 23, 2014 11:57 PM, "Roberto Congiu" <roberto.con...@openx.com>
>>> wrote:
>>>
>>>> Hi,
>>>> 1.1.4 is a oldish version of the JSON serde, have you tried with the
>>>> most recent from the master branch ?
>>>>
>>>>
>>>> On Mon, Jun 23, 2014 at 10:23 AM, Christian Link <
>>>> christian.l...@mdmp.com> wrote:
>>>>
>>>>> 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
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> ----------------------------------------------------------
>>>> Good judgement comes with experience.
>>>> Experience comes with bad judgement.
>>>> ----------------------------------------------------------
>>>> Roberto Congiu - Data Engineer - OpenX
>>>> tel: +1 626 466 1141
>>>>
>>>
>>
>
