I create table with this statement:
CREATE TABLE event(
date Date,
src UInt8,
channel UInt8,
deviceTypeId UInt8,
projectId UInt64,
shows UInt32,
clicks UInt32,
spent Float64
) ENGINE = MergeTree(date, (date, src, channel, projectId), 8192);
Raw data looks like:
{ "date":"2016-03-07T10:00:00+0300","src":2,"channel":18,"deviceTypeId ":101, "projectId":2363610,"shows":1232,"clicks":7,"spent":34.72,"location":"Unknown", ...}
...
Files with data loaded with the following command:
cat *.data|sed 's/T[0-9][0-9]:[0-9][0-9]:[0-9][0-9]+0300//'| clickhouse-client --query="INSERT INTO event FORMAT JSONEachRow"
clickhouse-client throw exception:
Code: 117. DB::Exception: Unknown field found while parsing JSONEachRow format: location: (at row 1)
Is it possible to skip fields from JSON object that not presented in table description?
The latest ClickHouse release (v1.1.54023) supports input_format_skip_unknown_fields user option which eneables skipping of unknown fields for JSONEachRow and TSKV formats.
Try
clickhouse-client -n --query="SET input_format_skip_unknown_fields=1; INSERT INTO event FORMAT JSONEachRow;"
See more details in documentation.
Currently, it is not possible to skip unknown fields.
You may create temporary table with additional field, INSERT data into it, and then do INSERT SELECT into final table. Temporary table may have Log engine and INSERT into that "staging" table will work faster than into final MergeTree table.
It is relatively easy to add possibility to skip unknown fields into code (something like setting 'format_skip_unknown_fields').
Related
My company is attempting to use Snowflake Named Internal Stages as a data lake to store vendor extracts.
There is a vendor that provides an extract that is 1000+ columns in a pipe delimited .dat file. This is a canned report that they extract. The column names WILL always remain the same. However, the column locations can change over time without warning.
Based on my research, a user can only query a file in a named internal stage using the following syntax:
--problematic because the order of the columns can change.
select t.$1, t.$2 from #mystage1 (file_format => 'myformat', pattern=>'.data.[.]dat.gz') t;
Is there anyway to use the column names instead?
E.g.,
Select t.first_name from #mystage1 (file_format => 'myformat', pattern=>'.data.[.]csv.gz') t;
I appreciate everyone's help and I do realize that this is an unusual requirement.
You could read these files with a UDF. Parse the CSV inside the UDF with code aware of the headers. Then output either multiple columns or one variant.
For example, let's create a .CSV inside Snowflake we can play with later:
create or replace temporary stage my_int_stage
file_format = (type=csv compression=none);
copy into '#my_int_stage/fx3.csv'
from (
select *
from snowflake_sample_data.tpcds_sf100tcl.catalog_returns
limit 200000
)
header=true
single=true
overwrite=true
max_file_size=40772160
;
list #my_int_stage
-- 34MB uncompressed CSV, because why not
;
Then this is a Python UDF that can read that CSV and parse it into an Object, while being aware of the headers:
create or replace function uncsv_py()
returns table(x variant)
language python
imports=('#my_int_stage/fx3.csv')
handler = 'X'
runtime_version = 3.8
as $$
import csv
import sys
IMPORT_DIRECTORY_NAME = "snowflake_import_directory"
import_dir = sys._xoptions[IMPORT_DIRECTORY_NAME]
class X:
def process(self):
with open(import_dir + 'fx3.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
yield(row, )
$$;
And then you can read this UDF that outputs a table:
select *
from table(uncsv_py())
limit 10
A limitation of what I showed here is that the Python UDF needs an explicit name of a file (for now), as it doesn't take a whole folder. Java UDFs do - it will just take longer to write an equivalent UDF.
https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-tabular-functions.html
https://docs.snowflake.com/en/user-guide/unstructured-data-java.html
My Use case-
Collect events for a particular duration and then group them based on the key
Objective
After processing, user can save data of particular duration based on the key
How i am planning to do
1)Receive events from Kafka
2)Create data stream of events
3)associate a table with it and collect data for a particular duration by running a SQL query
4)associate a new table with step-2 output and group collected data according to the key
5)save the data in DB
Solution i tried-
I am able to-
1)receive events from Kafka,
2)setup a data stream(lets say sensorDataStream)-
DataStream<SensorEvent> sensorDataStream
= source.flatMap(new FlatMapFunction<String, SensorEvent>() {
#Override
public void flatMap(String catalog, Collector<SensorEvent> out) {
// create SensorEvent(id, sensor notification value, notification time) creation
});
3)associate a table(lets say table1) with data stream and after running SQL query like-
SELECT id, sensorNotif, notifTime FROM SENSORTABLE WHERE notifTime > t1_Timestamp AND notifTime < t2_Timestamp.
Here t1_Timestamp and t2_Timestamp is predefined epoch time and will change based on some predefined conditions
4)I am able to print this sql query result by using following query on the console-
tableEnv.toAppendStream(table1, Row.class).print();
5)Created a new table(lets say table2) by using table1 and following type of sql query-
Table table2 = tableEnv.sqlQuery("SELECT id AS SensorID, COUNT(sensorNotif) AS SensorNotificationCount FROM table1 GROUP BY id);
6)Collecting and print data by using -
tableEnv.toRetractStream(table2 , Row.class).print();
Problem
1)I am not able to see output of step 6 on the console.
I did some experiment and found that If i skip table1 setup step(that means no sensor data clubbing for a duration) and directly associate my senserDataStream with table2 then i can see the output of step-6 but as this is RetractStream so i can see data in the form of and if new event is coming then this retract stream will invalidate data and print newly calculated data.
Suggestion i would like to have
1)How can i merge step 5 and step 6(means table1 and table2). I already merged these tables but as data is not visible on console so i have doubt? Am i doing something wrong? Or data is merged but not visible?
2)My plan is to --
2.a)filter data in 2 pass, in first pass filter data for a particular interval and in second pass group this data
2.b)Save 2.a output in DB
Will this approach work(i have doubt because i am using data stream and table1 out put is append stream but table2 output is retract stream)?
I have model I created on the fly for peewee. Something like this:
class TestTable(PeeweeBaseModel):
whencreated_dt = DateTimeField(null=True)
whenchanged = CharField(max_length=50, null=True)
I load data from a text file to a table using peewee, the column "whenchanged" contains all dates in a format of '%Y-%m-%d %H:%M:%S' as varchar column. Now I want to convert the text field "whenchanged" into a datetime format in "whencreated_dt".
I tried several things... I ended up with this:
# Initialize table to TestTable
to_execute = "table.update({table.%s : datetime.strptime(table.%s, '%%Y-%%m-%%d %%H:%%M:%%S')}).execute()" % ('whencreated_dt', 'whencreated')
which fails with a "TypeError: strptime() argument 1 must be str, not CharField": I'm trying to convert "whencreated" to datetime and then assign it to "whencreated_dt".
I tried a variation... following e.g. works without a hitch:
# Initialize table to TestTable
to_execute = "table.update({table.%s : datetime.now()}).execute()" % (self.name)
exec(to_execute)
But this is of course just the current datetime, and not another field.
Anyone knows a solution to this?
Edit... I did find a workaround eventually... but I'm still looking for a better solution... The workaround:
all_objects = table.select()
for o in all_objects:
datetime_str = getattr( o, 'whencreated' )
setattr(o, 'whencreated_dt', datetime.strptime(datetime_str, '%Y-%m-%d %H:%M:%S'))
o.save()
Loop over all rows in the table, get the "whencreated". Convert "whencreated" to a datetime, put it in "whencreated_dt", and save each row.
Regards,
Sven
Your example:
to_execute = "table.update({table.%s : datetime.strptime(table.%s, '%%Y-%%m-%%d %%H:%%M:%%S')}).execute()" % ('whencreated_dt', 'whencreated')
Will not work. Why? Because datetime.strptime is a Python function and operates in Python. An UPDATE query works in database-land. How the hell is the database going to magically pass row values into "datetime.strptime"? How would the db even know how to call such a function?
Instead you need to use a SQL function -- a function that is executed by the database. For example, Postgres:
TestTable.update(whencreated_dt=whenchanged.cast('timestamp')).execute()
This is the equivalent SQL:
UPDATE test_table SET whencreated_dt = CAST(whenchanged AS timestamp);
That should populate the column for you using the correct data type. For other databases, consult their manuals. Note that SQLite does not have a dedicated date/time data type, and the datetime functionality uses strings in the Y-m-d H:M:S format.
I'm having this problem with DBUnit causing a SQL insert error. Say I have this in my dbunit testdata.xml file:
<myschema.mytable id="1" value1="blah" value2="foo" />
I have a table like this (postgres)
myschema.mytable has an id, value1, value2, and a date field, say "lastmodified." The lastmodified column is timestamp with modifiers "not null default now()"
It appears that dbunit reads the table metadata and attempts to insert nulls for any column that isn't specified in my testdata.xml file. So the above xml results in an insert like this:
insert into myschema.mytable (id,value1,value2,lastmodified) values (1,'blah','foo',null)
When running tests (dbunit/maven plugin) I get an error like this:
Error executing database operation: REFRESH: org.postgresql.util.PSQLException: ERROR: null value in column "lastmodified" violates not-null constraint
Is there some way to tell DBUnit to NOT INSERT null values on fields that I don't specify?
Edit: Using dbunit 2.5.3, junit 4.12, postgressql driver 9.4.1208
Use the dbUnit "exclude column" feature:
How to exclude some table columns at runtime?
The FilteredTableMetaData class introduced in DbUnit 2.1 can be used in combination with the IColumnFilter interface to decide the inclusion or exclusion of table columns at runtime.
FilteredTableMetaData metaData = new FilteredTableMetaData(originalTable.getTableMetaData(), new MyColumnFilter());
ITable filteredTable = new CompositeTable(metaData, originalTable);
I have created a table :
add jar /../xlibs/hive-json-serde-0.2.jar;
CREATE EXTERNAL TABLE SerdeTest
(Unique_ID STRING
,MemberID STRING
,Data ARRAY>
)
PARTITIONED BY (Pyear INT, Pmonth INT)
ROW FORMAT SERDE "org.apache.hadoop.hive.contrib.serde2.JsonSerde";
ALTER TABLE SerdeTest ADD
PARTITION (Pyear = 2014, Pmonth =03) LOCATION '../Test2';
The data in the file :
{"Unique_ID":"ABC6800650654751","MemberID":"KHH966375835","Data":[{"SerialNo":1,"VariableName":"Var1","VariableValue":"A_49"},{"SerialNo":2,"VariableName":"Var2","VariableValue":"B_89"},{""SerialNo":3,"VariableName":"Var3","VariableValue":"A_99"}]}
Select query that I am using:
select Data[0].SerialNo from SerdeTest where Unique_ID = 'ABC6800650654751';
however, when I run this query I get the following error:
java.lang.RuntimeException: org.apache.hadoop.hive.ql.metadata.HiveException: Hive Runtime Error while processing row [Error getting row data with exception java.lang.ClassCastException: org.json.JSONArray cannot be cast to [Ljava.lang.Object;
at org.apache.hadoop.hive.serde2.objectinspector.StandardListObjectInspector.getList(StandardListObjectInspector.java:98)
at org.apache.hadoop.hive.serde2.SerDeUtils.buildJSONString(SerDeUtils.java:330)
at org.apache.hadoop.hive.serde2.SerDeUtils.buildJSONString(SerDeUtils.java:386)
at org.apache.hadoop.hive.serde2.SerDeUtils.getJSONString(SerDeUtils.java:237)
at org.apache.hadoop.hive.serde2.SerDeUtils.getJSONString(SerDeUtils.java:223)
at org.apache.hadoop.hive.ql.exec.MapOperator.process(MapOperator.java:539)
at org.apache.hadoop.hive.ql.exec.mr.ExecMapper.map(ExecMapper.java:157)
at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:50)
at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:418)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:349)
at org.apache.hadoop.mapred.Child$4.run(Child.java:270)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1127)
at org.apache.hadoop.mapred.Child.main(Child.java:264)
]
Can anyone please suggest me what am I doing wrong
Few suggestions:
Make sure that all the packages of hive and hive-json-serde-0.2.jar have execute permission for hadoop user.
Hive creates a file called derby.log and metastore_db in the hive directory. It should be allowed to the user invoking the hive query to create files and directories.
Location for data should have / at the end. e.g. LOCATION '../Test2/';
In short, the working JAR is json-serde-1.3-jar-with-dependencies.jar which can be found here. This one is working with 'STRUCT' and can even ignore some malformed JSON. During the creation of the table, include the following code:
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe'
WITH SERDEPROPERTIES ("ignore.malformed.json" = "true")
LOCATION ...
If needed, it is possible to recompile it from here or here. I tried the first repository and it is compiling fine for me, after adding the necessary libs. The repository has also been updated recently.
Check for more details here.