I am loading parquet file into BigQuery using bq load command, my parquet file contains column name start with number (e.g. 00_abc, 01_xyz). since BigQuery don't support column name start number I have created column in BigQuery such as _00_abc, _01_xyz.
But I am unable to load the parquet file to BigQuery using bq load command.
Is there any way to specify bq load command that source column 00_abc (from parquet file) will load to target column _00_abc (in BigQuery).
Thanks in advance.
Regards,
Gouranga Basak
It's general best practice to not start a Parquet column name with a number. You will experience compatibility issues with more than just bq load. For example, many Parquet readers use the parquet-avro library, and Avro's documentation says:
The name portion of a fullname, record field names, and enum symbols must:
start with [A-Za-z_]
subsequently contain only [A-Za-z0-9_]
The solution here is to rename the column in the Parquet file. Depending on how much control you have over the Parquet file's creation, you may need to write a Cloud Function to rename the columns (Pandas Dataframes won't complain about your column names).
Just starting out with bigquery and trying to find the best way to upload db tables to bq. We've been converting the table content to avro using the avsc library because from all the docs it seems that avro is the fastest way to load it but it adds a root element to the schema so that all the columns are root.name, root.time etc. I saw there was another post about this from 2016 here and the solution is to use a temporary table and strip out the root like this,
bq query --external_table_definition=foo::AVRO=gs://your_bucket/path/file.avro* --destination_table your_dataset.your_table "SELECT root.* FROM foo"
but the nodejs library only has instructions to accomplish permanent tables, not temporary ones. Even if I wanted to create a permanent table, I can't because due to the "root" it places all columns in one row and the amount of data exceeds the amount allowed in a single row. How can I load the data to bigquery?
You can create an external table using the bq js library [1] you need to set the options object appropriately [2]
[1] https://github.com/googleapis/nodejs-bigquery/blob/master/samples/createTable.js
[2] https://stackoverflow.com/a/42916251/5873699
I am going to work on a data set that contains information about 311 calls in the United States. This data set is available publicly in BigQuery. I would like to copy this directly to my bucket. However, I am clueless about how to do this as I am a novice.
Here is a screenshot of the public location of the dataset on Google Cloud:
I have already created a bucket named 311_nyc in my Google Cloud Storage. How can I directly transfer the data without having to download the 12 gb file and uploading it again through my VM instance?
If you select the 311_service_requests table from the list on the left, an "Export" button will appear:
Then you can select Export to GCS, select your bucket, type a filename, choose format (between CSV and JSON) and check if you want the export file to be compressed (GZIP).
However, there are some limitations in BigQuery Exports. Copying some from the documentation link that apply to your case:
You can export up to 1 GB of table data to a single file. If you are exporting more than 1 GB of data, use a wildcard to export the data into multiple files. When you export data to multiple files, the size of the files will vary.
When you export data in JSON format, INT64 (integer) data types are encoded as JSON strings to preserve 64-bit precision when the data is read by other systems.
You cannot choose a compression type other than GZIP when you export data using the Cloud Console or the classic BigQuery web UI.
EDIT:
A simple way to merge the output files together is to use the gsutil compose command. However, if you do this the header with the column names will appear multiple times in the resulting file because it appears in all the files that are extracted from BigQuery.
To avoid this, you should perform the BigQuery Export by setting the print_header parameter to False:
bq extract --destination_format CSV --print_header=False bigquery-public-data:new_york_311.311_service_requests gs://<YOUR_BUCKET_NAME>/nyc_311_*.csv
and then create the composite:
gsutil compose gs://<YOUR_BUCKET_NAME>/nyc_311_* gs://<YOUR_BUCKET_NAME>/all_data.csv
Now, in the all_data.csv file there are no headers at all. If you still need the column names to appear in the first row you have to create another CSV file with the column names and create a composite of these two. This can be done either manually by pasting the following (column names of the "311_service_requests" table) into a new file:
unique_key,created_date,closed_date,agency,agency_name,complaint_type,descriptor,location_type,incident_zip,incident_address,street_name,cross_street_1,cross_street_2,intersection_street_1,intersection_street_2,address_type,city,landmark,facility_type,status,due_date,resolution_description,resolution_action_updated_date,community_board,borough,x_coordinate,y_coordinate,park_facility_name,park_borough,bbl,open_data_channel_type,vehicle_type,taxi_company_borough,taxi_pickup_location,bridge_highway_name,bridge_highway_direction,road_ramp,bridge_highway_segment,latitude,longitude,location
or with the following simple Python script (in case you want to use it with a table with a big amount of columns that is hard to be done manually) that queries the column names of the table and writes them into a CSV file:
from google.cloud import bigquery
client = bigquery.Client()
query = """
SELECT column_name
FROM `bigquery-public-data`.new_york_311.INFORMATION_SCHEMA.COLUMNS
WHERE table_name='311_service_requests'
"""
query_job = client.query(query)
columns = []
for row in query_job:
columns.append(row["column_name"])
with open("headers.csv", "w") as f:
print(','.join(columns), file=f)
Note that for the above script to run you need to have the BigQuery Python Client library installed:
pip install --upgrade google-cloud-bigquery
Upload the headers.csv file to your bucket:
gsutil cp headers.csv gs://<YOUR_BUCKET_NAME/headers.csv
And now you are ready to create the final composite:
gsutil compose gs://<YOUR_BUCKET_NAME>/headers.csv gs://<YOUR_BUCKET_NAME>/all_data.csv gs://<YOUR_BUCKET_NAME>/all_data_with_headers.csv
In case you want the headers you can skip creating the first composite and just create the final one using all sources:
gsutil compose gs://<YOUR_BUCKET_NAME>/headers.csv gs://<YOUR_BUCKET_NAME>/nyc_311_*.csv gs://<YOUR_BUCKET_NAME>/all_data_with_headers.csv
You can also use the gcoud commands:
Create a bucket:
gsutil mb gs://my-bigquery-temp
Extract the data set:
bq extract --destination_format CSV --compression GZIP 'bigquery-public-data:new_york_311.311_service_requests' gs://my-bigquery-temp/dataset*
Please note that you have to use gs://my-bigquery-temp/dataset* because the dataset is to large and can not be exported to a single file.
Check the bucket:
gsutil ls gs://my-bigquery-temp
gs://my-bigquery-temp/dataset000000000
......................................
gs://my-bigquery-temp/dataset000000000045
You can find more information Exporting table data
Edit:
To compose an object from the exported dataset files you can use gsutil tool:
gsutil compose gs://my-bigquery-temp/dataset* gs://my-bigquery-temp/composite-object
Please keep in mind that you can not use more that 32 blobs (files) to compose the object.
Related SO Question Google Cloud Storage Joining multiple csv files
We have couple HDFS directories in which data stored in delimited format. These directories created as one directory per ingestion date. These directories added as a partitions to a Hive external table.
Directory structure:
/data/table1/INGEST_DATE=20180101
/data/table1/INGEST_DATE=20180102
/data/table1/INGEST_DATE=20180103 etc.
Now we want to process this data in spark job. From the program I can directly read these HDFS directories by giving exact directory path(Option 1) or I can read from Hive into a data frame and process(Option 2).
I would like to know if there is any significant difference in following Option1 or Option2. Please let me know if need any other details.
Thanks in Advance
If you want to select a subset of the columns, then that it is only possible via spark.sql. In your use case I don't think there will be a significant difference.
With Spark SQL you can get Partition pruning automatically.
I am exporting data from DynamoDB to S3 using follwing script:
CREATE EXTERNAL TABLE TableDynamoDB(col1 String, col2 String)
STORED BY 'org.apache.hadoop.hive.dynamodb.DynamoDBStorageHandler' TBLPROPERTIES (
"dynamodb.table.name" = "TableDynamoDB",
"dynamodb.column.mapping" = "col1:col1,col2:col2"
);
CREATE EXTERNAL TABLE TableS3(col1 String, col2 String)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
LOCATION 's3://myBucket/DataFiles/MyData.txt';
INSERT OVERWRITE TABLE TableS3
SELECT * FROM TableDynamoDB;
In S3, I want to write the output to a given file name (MyData.txt)
but the way it is working currently is that above script created folder with name 'MyData.txt'
and then generated a file with some random name under this folder.
Is it at all possible to specify a file name in S3 using HIVE?
Thank you!
A few things:
There are 2 different ways hadoop can write data to s3. This wiki describes the differences in a little more detail. Since you are using the "s3" scheme, you are probably seeing a block number.
In general, M/R jobs (and hive queries) are going to want to write their output to multiple files. This is an artifact of parallel processing. In practice, most commands/APIs in hadoop handle directories pretty seamlessly so you shouldn't let it bug you too much. Also, you can use things like hadoop fs -getmerge on a directory to read all of the files in a single stream.
AFAIK, the LOCATION argument in the DDL for an external hive table is always treated as a directory for the reasons above.