I have successfully loaded large number of AVRO files (of same schema type into same table), stored on Google Storage, using bq CLI utility.
However, for some of the AVRO files I am getting very cryptic error while loading into bigquery, the error says:
The Apache Avro library failed to read data with the follwing error: EOF
reached (error code: invalid)
With avro-tools validated that the AVRO file is not corrupted, report output:
java -jar avro-tools-1.8.1.jar repair -o report 2017-05-15-07-15-01_48a99.avro
Recovering file: 2017-05-15-07-15-01_48a99.avro
File Summary:
Number of blocks: 51 Number of corrupt blocks: 0
Number of records: 58598 Number of corrupt records: 0
I tried creating a brand new table with one of the failing files in case it was due to schema mismatch but that didnt help as the error was exactly the same.
need help to figure out what could be causing the error here?
No way to pinpoint the issue without more information, but I ran into this error message and filed a ticket here.
I a number of files in a single load job were missing columns which was causing the error.
Explanation from the ticket.
BigQuery uses the alphabetically last file from the directory as the avro schema to read the other Avro files. I suspect the issue is with schema incompatibility between the last file and the "problematic" file. Do you know if all the files have the exact same schema or differ? One thing you could try to help verify this is to copy the alphabetically last file of the directory and the "problematic" file to a different folder and try to load those two files in one BigQuery load job and see if the error reproduces.
Related
I created a table using Athena CTAS statements. Per Glue, I see that the table is stored on my s3 bucket. I further confirmed that there are files in the expected place in my s3 bucket.
These files, however, are not parquet files (they are extension-less). When I try to read them into python using pd.read_parquet, I get the Error "Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.". A similar error occurs when I try to query the table and read the csv output using pd.read_csv. There, the error is "'utf-8' codec can't decode byte 0xee in position 0: invalid continuation byte". I tried using awswrangler and got the same errors.
I'm pretty sure these errors are related to the SSE_S3 encryption I put on the bucket. However, I'm at a loss as to how I can actually interact with these files outside of Athena.
The resolution is that the default Athena workgroup had CSE_KMS encryption turned on. I couldn't quickly figure out how to pass these options via awswrangler, so I took the shortcut of recreating the table using another workgroup that didn't have encryption.
I created a Vora table in Vora 1.3 and tried to append data to that table from ORC files that I got from SAP BW archiving process (NLS on Hadoop). I had 20 files, in total containing approx 50 Mio records.
When I tried to use the "files" setting in the APPEND statement as "/path/*", after approx 1 hour Vora returned this error message:
com.sap.spark.vora.client.VoraClientException: Could not load table F002_5F: [Vora [eba156.extendtec.com.au:42681.1640438]] java.lang.RuntimeException: Wrong magic number in response, expected: 0x56320170, actual: 0x00000000. An unsuccessful attempt to load a table might lead to an inconsistent table state. Please drop the table and re-create it if necessary. with error code 0, status ERROR_STATUS
Next thing I tried was appending data from each file using separate APPEND statements. On the 15th append (of 20) I've got the same error message.
The error indicates that the Vora engine on node eba156.extendtec.com.au is not available. I suspect it either crashed or ran into an out-of-memory situtation.
You can check the log directory for a crash dump. If you find one, please open a customer message for further investigation.
If you do not find a crash dump, it is likely a out-of-memory situation. You should find confirmation in either the engine log file or in /var/log/messages (if the oom killer ended the process). In that case, the available memory is not sufficient to load the data.
I have a set of avro files with slightly varying schemas which I'd like to load into one bq table.
Is there a way to do that with one line? Every automatic way to handle schema difference would be fine for me.
Here is what I tried so far.
0) If I try to do it in a straightforward way, bq fails with error:
bq load --source_format=AVRO myproject:mydataset.logs gs://mybucket/logs/*
Waiting on bqjob_r4e484dc546c68744_0000015bcaa30f59_1 ... (4s) Current status: DONE
BigQuery error in load operation: Error processing job 'iow-rnd:bqjob_r4e484dc546c68744_0000015bcaa30f59_1': The Apache Avro library failed to read data with the follwing error: EOF reached
1) Quick googling shows that there is --schema_update_option=ALLOW_FIELD_ADDITION option which, added to bq load job, changes nothing. ALLOW_FIELD_RELAXATION does not change anything either.
2) Actually, schema id is mentioned in the file name, so files look like:
gs://mybucket/logs/*_schemaA_*
gs://mybucket/logs/*_schemaB_*
Unfortunately, bq load does not allow more that on asterisk (as is written in bq manual too):
bq load --source_format=AVRO myproject:mydataset.logs gs://mybucket/logs/*_schemaA_*
BigQuery error in load operation: Error processing job 'iow-rnd:bqjob_r5e14bb6f3c7b6ec3_0000015bcaa641f3_1': Not found: Uris gs://otishutin-eu/imp/2016-06-27/*_schemaA_*
3) When I try to list the files explicitly, the list happens to be too long, so bq load does not work either:
bq load --source_format=AVRO myproject:mydataset.logs $(gsutil ls gs://mybucket/logs/*_schemaA_* | xargs | tr ' ' ',')
Too many positional args, still have ['gs://mybucket/logs/log_schemaA_2658.avro,gs://mybucket/logs/log_schemaA_2659.avro,gs://mybucket/logs/log_schemaA_2660.avro,...
4) When I try to use files as external table and list the files explicitly in external table definition, I also get "too many files" error:
BigQuery error in query operation: Table definition may not have more than 500 source_uris
I understand that I could first copy files to different folders and then process them folder-by-folder, and this is what I'm doing now as last resort, but this is only a small part of data processing pipeline, and copying is not acceptable as production solution.
Google BigQuery has on March 23, 2016 announced "Added support for Avro source format for load operations and as a federated data source in the BigQuery API or command-line tool". It says here "This is a Beta release of Avro format support. This feature is not covered by any SLA or deprecation policy and may be subject to backward-incompatible changes.". However, I'd expect the feature to work.
I didn't find anywhere code examples on how to use Avro format for loading. Neither I did find examples on how to use bq-tool for loading.
Here's my practical issue. I haven't been able to load data into BigQuery in Avro-format.
The following happens using bq-tool. The dataset, table name and bucket name have been obfuscated:
$ bq extract --destination_format=AVRO dataset.events_avro_test gs://BUCKET/events_bq_tool.avro
Waiting on bqjob_r62088699049ce969_0000015432b7627a_1 ... (36s) Current status: DONE
$ bq load --source_format=AVRO dataset.events_avro_test gs://BUCKET/events_bq_tool.avro
Waiting on bqjob_r6cefe75ece6073a1_0000015432b83516_1 ... (2s) Current status: DONE
BigQuery error in load operation: Error processing job 'dataset:bqjob_r6cefe75ece6073a1_0000015432b83516_1': An internal error occurred and the request could not be completed.
Basically, I am extracting from a table and inserting to the same table causing an internal error.
Additionally, I have Java program that does the same (extract from table X and load to table X) with the same result (internal error). But I think the above illustrates the problem as clearly as possible, and because of that I'm not sharing the code here. In Java, If I extract from an empty table and insert that, the insert job doesn't fail.
My questions are
I think BigQuery API should never fail with internal error. Why is that happening with my test?
Is the extracted Avro file compatible with an insert job?
There seems to be no specification what the Avro schema in an insert job is like, at least I couldn't find any. Could the documentation be created?
UPDATED 2016-04-25:
So far I've managed to get an Avro load job not to give an internal error based on the hint of not using REQUIRED fields. However, I haven't managed to load non-null values.
Consider this Avro-schema:
{
"type": "record",
"name": "root",
"fields": [
{
"name": "x",
"type": "string"
}
]
}
The BigQuery table has one column, x that is NULLABLE.
If I insert N (I've tried with one and two) rows (x being e.g. 1), I got N rows in BigQuery but x always having value null.
If I change the table so that X is REQUIRED I get an internal error.
There is no exact match from a BQ schema to Avro schema, and vice versa, so when you export a BQ table to Avro file and then import back, the schema will be different. I see the destination table of your load already exists, in this case we throw an error when the schema of the destination table doesn't match the schema we converted from the Avro schema. This should be an external error though, we're investigating why it's an internal error.
We're in the middle of upgrading the export pipeline, and the new import pipeline has a bug that doesn't work with the Avro file exported by the current pipeline. The fix should be deployed in a couple weeks. After that, if you import the exported file to a non-existent destination table, or a destination table with compatible schema, it should work. Meanwhile, importing your own Avro files should work. You can also query it directly on GCS without importing it.
There's a problem with the error mapping for the AVRO reader here. The error should have been along the lines of: "The reference schema differs from the existing data: The required field 'api_key' is missing"
Looking at your load job configuration, it includes REQUIRED fields. It sounds like some of the data you are trying to load doesn't specify these required fields, so the operation fails.
I suggest avoiding required fields.
So, there's a bug in BigQuery: an insert job using Avro format does not work if the destination table exists, but gives an internal error. The workaround is to use createDisposition CREATE_IF_NEEDED and not to have the pre-existing table there. I verified that this works.
Hua Zung's comment says that the bug will be fixed in "the fix should be deployed in a couple weeks". Needless to say that existing major bugs in the live system should be documented somewhere.
While updating the system, I really recommend improving the Avro documentation. Now there's no mention on what the Avro schema should be like (type record, name root and fields array having the columns(?)) and not even the fact that each record in the Avro file maps to a row in the destination table (obvious, but should be mentioned). Also what happens with schema mismatch is not documented.
Thanks for the help, I'll be now switching to Avro-format. It's so much better than CSV.
I am trying to write a Pig Script for compacting small files having data in the parquet format. Below mentioned lines are trying to load the small files in the directory and then store them. The files have complex nested structures which are nullable and they contain lots of the NULLs.
LOGS = LOAD '/dt=20150307/hr=2015030700/*' USING parquet.pig.ParquetLoader();
STORE LOGS INTO '/user/compaction_output' USING parquet.pig.ParquetStorer();
I am getting the following error:
2015-04-29 17:00:45,883 [main] ERROR org.apache.pig.tools.pigstats.SimplePigStats - ERROR 2118: Cannot build an empty group
My suspicion is that it is because of the null values in the input files.
Can someone help out ?