I have a nested json as my source file in S3 and I am trying to copy this file into redshift.
My issues with this are as follows,
I use MAXERROR - I need to skip certain errors because the source file is missing certain fields in some cases and has them in other
I use a JSONPATH file - to pick the fields that I need to copy to redshift
All the columns in the table are varchar
Obviously, since I am using maxerror the copy command executes successfully but the table has 0 records. Here is my copy command
COPY public.table(col1,col2,col3,col4,col5,col6)
from 's3://bucket/filename'
credentials 'redshift'
format as JSON 'jsonpathfile.json'
timeformat 'YYYY-MM-DDTHH:MI:SS'
EMPTYASNULL ACCEPTANYDATE ACCEPTINVCHARS TRUNCATECOLUMNS maxerror 100 ;
If I check into stl_load_errors it keeps saying
Invalid JSONPath format: Member is not an object.
Does this mean the copy command is not able to find even one object that fits the jsonpath file?
Which is definitely not true. I inferred the schema of the input file to design the jsonpath file.
Here is an example from COPY Examples - Amazon Redshift:
copy category
from 's3://mybucket/category_object_paths.json'
iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole'
json 's3://mybucket/category_jsonpath.json';
The path to the jsonpath file is specified fully, whereas your example just refers to the filename.
Try specifying the full path starting with s3:// and see whether that helps.
Related
I need to restore a lot of object from aws s3 glacier deep archive. So i try to use a s3 batch jobs. For that i use a python code to create a manifest as a csv with to columns Bucket,Key.
But my first issue : some Key contain a comma so the job failed.
To solve (partialy) this issue i just cut the csv file to keep only the first two columns hoping that there are not many files involved.
But now i have another issue:
ErrorMessage: Task target couldn't be URL decoded
Any Idea ?
As mentioned on https://docs.aws.amazon.com/AmazonS3/latest/userguide/batch-ops-create-job.html#specify-batchjob-manifest, the manifest CSV file must be URL encoded. The , character in a key name gets converted to %2C with URL encoding so the resulting file will be valid CSV even with commas in the key name
I have created a pyspark dataframe and trying to write the file in s3 bucket in csv format. here the file is writing in csv but the issue is it's writing the file twice(i.e., with actual data and another is with empty data). I have checked the data frame by printing fine only. please suggest any way to prevent that empty wouldn't create.
code snippet:
df = spark.createDataFrame(data=dt1, schema = op_df.columns)
df.write.option("header","true").csv("s3://"+ src_bucket_name+"/src/output/"+row.brand +'/'+fileN)
One possible solution to make sure that the output will include only one file is to do repartition(1) or coalesce(1) before writing.
So something like this:
df.repartition(1).write.option("header","true").csv("s3://"+ src_bucket_name+"/src/output/"+row.brand +'/'+fileN)
Note that having one partition doesn't not necessarily mean that it will result in one file as this can depend on the spark.sql.files.maxRecordsPerFile configuration as well. Assuming this config is set to 0 (the default) you should get only 1 file in the output.
I am trying to load data which is stored in .gz format in S3 to PostgreSQL server using Datastage. I am using the ODBC connector on the target (database) side. I am able to load uncompressed data from S3 to PostgreSQL but no luck with compressed data so far. I have tried the Expand Stage but it's not helping or I am not doing the right thing. Without the "Expand" the data is coming but it is trying to read the compressed data, while doing so it fails and throws an error:
Amazon_S3_0,1: com.ascential.e2.common.CC_Exception: Failed to initialize the parser: The row delimiter was not found within the first 132 bytes of the file. Ensure that the Row delimiter property matches the row delimiter of the file.
at com.ibm.iis.cc.cloud.CloudLogger.createCCException (CloudLogger.java: 196)
at com.ibm.iis.cc.cloud.CloudStage.processReadAndParse (CloudStage.java: 1591)
at com.ibm.iis.cc.cloud.CloudStage.process (CloudStage.java: 680)
at com.ibm.is.cc.javastage.connector.CC_JavaAdapter.run (CC_JavaAdapter.java: 443)
Amazon_S3_0,1: Failed to initialize the parser: The row delimiter was not found within the first 132 bytes of the file. Ensure that the Row delimiter property matches the row delimiter of the file. (com.ibm.iis.cc.cloud.CloudLogger::createCCException, file CloudLogger.java, line 196)
If someone has come across this, please share your valuable inputs.
I am using the template to do a copy. In the "S3" data node, I have configured Custom format because my column data contains "comma".
The custom data format I am using has the following properties
Column seperator " is PIPE ("|") and Record seperator is "\n"
When I run the pipeline data does not get loaded into S3 and gives me the following error
Error copying record\nCause: java.lang.reflect.InvocationTargetException\nCause: null\nCause: Quote character must be defined in record format
Anyone faced this problem.
"Escape Char" option is not shown under "Custom" format type in AWS Data pipeline
You can use "TSV" type as your custom format type and provide:
"Column separator" as pipe(|),
"Record separator" as new line(\n),
"Escape Char" as backslash(\) or any other character you want.
If you are using Aurora as source database, I suggest you use SELECT INTO OUTFILE S3 command. Otherwise, build your own using EMR (hive and sqoop projects will help you with this).
I want to know how using BQ command line tool I can change configuration of a BigQuery API job. E.g., I want to change configuration.load.quote property from command line tool. Is there is any way. I need this to load a table with field double quote(") inside.
You cannot modify a job once it is created, but I guess what you want is set the quote property when creating the job.
In most cases, bq help <command> will get you what you need. Here's the output of bq help load. As you can see, you just have to specify --quote="'" after the command but before the arguments.
$ bq help load
Python script for interacting with BigQuery.
USAGE: bq.py [--global_flags] <command> [--command_flags] [args]
load Perform a load operation of source into destination_table.
Usage:
load <destination_table> <source> [<schema>]
The <destination_table> is the fully-qualified table name of table to
create, or append to if the table already exists.
The <source> argument can be a path to a single local file, or a
comma-separated list of URIs.
The <schema> argument should be either the name of a JSON file or a
text schema. This schema should be omitted if the table already has
one.
In the case that the schema is provided in text form, it should be a
comma-separated list of entries of the form name[:type], where type
will default to string if not specified.
In the case that <schema> is a filename, it should contain a single
array object, each entry of which should be an object with properties
'name', 'type', and (optionally) 'mode'. See the online documentation
for more detail:
https://developers.google.com/bigquery/preparing-data-for-bigquery
Note: the case of a single-entry schema with no type specified is
ambiguous; one can use name:string to force interpretation as a
text schema.
Examples:
bq load ds.new_tbl ./info.csv ./info_schema.json
bq load ds.new_tbl gs://mybucket/info.csv ./info_schema.json
bq load ds.small gs://mybucket/small.csv name:integer,value:string
bq load ds.small gs://mybucket/small.csv field1,field2,field3
Arguments:
destination_table: Destination table name.
source: Name of local file to import, or a comma-separated list of
URI paths to data to import.
schema: Either a text schema or JSON file, as above.
Flags for load:
/home/David/google-cloud-sdk/platform/bq/bq.py:
--[no]allow_jagged_rows: Whether to allow missing trailing optional columns in
CSV import data.
--[no]allow_quoted_newlines: Whether to allow quoted newlines in CSV import
data.
-E,--encoding: <UTF-8|ISO-8859-1>: The character encoding used by the input
file. Options include:
ISO-8859-1 (also known as Latin-1)
UTF-8
-F,--field_delimiter: The character that indicates the boundary between
columns in the input file. "\t" and "tab" are accepted names for tab.
--[no]ignore_unknown_values: Whether to allow and ignore extra, unrecognized
values in CSV or JSON import data.
--max_bad_records: Maximum number of bad records allowed before the entire job
fails.
(default: '0')
(an integer)
--quote: Quote character to use to enclose records. Default is ". To indicate
no quote character at all, use an empty string.
--[no]replace: If true erase existing contents before loading new data.
(default: 'false')
--schema: Either a filename or a comma-separated list of fields in the form
name[:type].
--skip_leading_rows: The number of rows at the beginning of the source file to
skip.
(an integer)
--source_format: <CSV|NEWLINE_DELIMITED_JSON|DATASTORE_BACKUP>: Format of
source data. Options include:
CSV
NEWLINE_DELIMITED_JSON
DATASTORE_BACKUP
gflags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on
the command line even if the program does not define a flag with that name.
IMPORTANT: flags in this list that have arguments MUST use the --flag=value
format.
(default: '')