I have created an impala table as
create table my_schema.my_table stored as textfile as select ...
As per the definition the table has data stored in textfiles somewhere in HDFS. Now when i run hdfs command such as:
hadoop fs -cat path_to_file | head
I do not see any column names. I suppose impala stores the column names somewhere else, but since i would like to work with these textfiles also outside of impala, it would be great if the files would include the headers.
Is there some option i can set when creating the table to add the headers to the text files? Or do i need to figure out the names by parsing the results of show create table?
Related
I have a Power BI report where I load data from a CSV into a table (Original Table). I want to change the source of the table to a folder that contains multiple CSVs. The issue i'm having is that when I go to change the data source settings of my Original Table I cannot change it to folder. Original Table has many measures and I would like to avoid rewriting them all. Any ideas?
Create a new query that reads multiple csv from a folder and combines them.
Make sure the column names are the same as in your original table.
Open both queries in the advanced editor and copy the whole code from the new query to the old query
Delete the new query
I have a table in Snowflake that have 24 columns in it. And I also have a CSV files in the S3 bucket with the changing number of columns. Sometimes it can be 4 columns and sometimes it can be 24 columns etc. I need also map the names of the CSV files columns to the name of the Snowflake table columns. Is there any way how to do it?
You would need to pre-process your CSV file to bring it to a more consistent format as your target table.
You can extract the column header and generate a COPY command mapping those columns into your table.
I have a setup with Kinesis Firehose ingesting data, AWS Lambda performing data transformation and dropping the incoming data into an S3 bucket. The S3 structure is organized by year/month/day/hour/messages.json, so all of the actual json files I am querying are at the 'hour' level with all year, month, day directories only containing sub directories.
My problem is I need to run a query to get all data for a given day. Is there an easy way to query at the 'day' directory level and return all files in its sub directories without having to run a query for 2020/06/15/00, 2020/06/15/01, 2020/06/15/02...2020/06/15/23?
I can successfully query the hour level directories since I can create a table and define the column name and type represented in my .json file, but I am not sure how to create a table in Athena (if possible) to represent a day directory with sub directories instead of actual files.
To query only the data for a day without making Athena read all the data for all days you need to create a partitioned table (look at the second example). Partitioned tables are like regular tables, but they contain additional metadata that describes where the data for a particular combination of the partition keys is located. When you run a query and specify criteria for the partition keys Athena can figure out which locations to read and which to skip.
How to configure the partition keys for a table depends on the way the data is partitioned. In your case the partitioning is by time, and the timestamp has hourly granularity. You can choose a number of different ways to encode this partitioning in a table, which one is the best depends on what kinds of queries you are going to run. You say you want to query by day, which makes sense, and will work great in this case.
There are two ways to set this up, the traditional, and the new way. The new way uses a feature that was released just a couple of days ago and if you try to find more examples of it you may not find many, so I'm going to show you the traditional too.
Using Partition Projection
Use the following SQL to create your table (you have to fill in the columns yourself, since you say you've successfully created a table already just use the columns from that table – also fix the S3 locations):
CREATE EXTERNAL TABLE cszlos_firehose_data (
-- fill in your columns here
)
PARTITIONED BY (
`date` string
)
ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'
LOCATION 's3://cszlos-data/is/here/'
TBLPROPERTIES (
"projection.enabled" = "true",
"projection.date.type" = "date",
"projection.date.range" = "2020/06/01,NOW",
"projection.date.format" = "yyyy/MM/dd",
"projection.date.interval" = "1",
"projection.date.interval.unit" = "DAYS",
"storage.location.template" = "s3://cszlos-data/is/here/${date}"
)
This creates a table partitioned by date (please note that you need to quote this in queries, e.g. SELECT * FROM cszlos_firehose_data WHERE "date" = …, since it's a reserved word, if you want to avoid having to quote it use another name, dt seems popular, also note that it's escaped with backticks in DDL and with double quotes in DML statements). When you query this table and specify a criteria for date, e.g. … WHERE "date" = '2020/06/05', Athena will read only the data for the specified date.
The table uses Partition Projection, which is a new feature where you put properties in the TBLPROPERTIES section that tell Athena about your partition keys and how to find the data – here I'm telling Athena to assume that there exists data on S3 from 2020-06-01 up until the time the query runs (adjust the start date necessary), which means that if you specify a date before that time, or after "now" Athena will know that there is no such data and not even try to read anything for those days. The storage.location.template property tells Athena where to find the data for a specific date. If your query specifies a range of dates, e.g. … WHERE "date" > '2020/06/05' Athena will generate each date (controlled by the projection.date.interval property) and read data in s3://cszlos-data/is/here/2020-06-06, s3://cszlos-data/is/here/2020-06-07, etc.
You can find a full Kinesis Data Firehose example in the docs. It shows how to use the full hourly granularity of the partitioning, but you don't want that so stick to the example above.
The traditional way
The traditional way is similar to the above, but you have to add partitions manually for Athena to find them. Start by creating the table using the following SQL (again, add the columns from your previous experiments, and fix the S3 locations):
CREATE EXTERNAL TABLE cszlos_firehose_data (
-- fill in your columns here
)
PARTITIONED BY (
`date` string
)
ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'
LOCATION 's3://cszlos-data/is/here/'
This is exactly the same SQL as above, but without the table properties. If you try to run a query against this table now you will not get any results. The reason is that you need to tell Athena about the partitions of a partitioned table before it knows where to look for data (partitioned tables must have a LOCATION, but it really doesn't mean the same thing as for regular tables).
You can add partitions in many different ways, but the most straight forward for interactive use is to use ALTER TABLE ADD PARTITION. You can add multiple partitions in one statement, like this:
ALTER TABLE cszlos_firehose_data ADD
PARTITION (`date` = '2020-06-06') LOCATION 's3://cszlos-data/is/here/2020/06/06'
PARTITION (`date` = '2020-06-07') LOCATION 's3://cszlos-data/is/here/2020/06/07'
PARTITION (`date` = '2020-06-08') LOCATION 's3://cszlos-data/is/here/2020/06/08'
PARTITION (`date` = '2020-06-09') LOCATION 's3://cszlos-data/is/here/2020/06/09'
If you start reading more about partitioned tables you will probably also run across the MSCK REPAIR TABLE statement as a way to load partitions. This command is unfortunately really slow, and it only works for Hive style partitioned data (e.g. …/year=2020/month=06/day=07/file.json) – so you can't use it.
I am following this example:
LazySimpleSerDe for CSV, TSV, and Custom-Delimited Files - TSV example
Summary of the code:
CREATE EXTERNAL TABLE flight_delays_tsv (
yr INT,
quarter INT,
month INT,
...
div5longestgtime INT,
div5wheelsoff STRING,
div5tailnum STRING
)
PARTITIONED BY (year STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
ESCAPED BY '\\'
LINES TERMINATED BY '\n'
LOCATION 's3://athena-examples-myregion/flight/tsv/';
My questions are:
My tsv does not have column names
(my tsv)
Is it ok if I just list the columns as c1,c2… and all of them as string ?
I do not understand this:
PARTITIONED BY (year STRING)
in the example, the column ‘year’ is not listed in any of the columns…
Column names
The column names are defined by the CREATE EXTERNAL TABLE command. I recommend you name them something useful so that it is easier to write queries. The column names do not need to match any names in the actual file. (It does not interpret header rows.)
Partitioning
From Partitioning Data - Amazon Athena:
To create a table with partitions, you must define it during the CREATE TABLE statement. Use PARTITIONED BY to define the keys by which to partition data.
The field used to partition the data is NOT stored in the files themselves, which is why they are not in the table definition. Rather, the column value is stored in the name of the directory.
This might seem strange (storing values in a directory name!) but actually makes sense because it avoids situations where an incorrect value is stored in a folder. For example, if there is a year=2018 folder, what happens if a file contains a column where the year is 2017? This is avoided by storing the year in the directory name, such that any files within that directory are assigned the value denoted in the directory name.
Queries can still use WHERE year = 2018 even though it isn't listed as an actual column.
See also: LanguageManual DDL - Apache Hive - Apache Software Foundation
The other neat thing is that data can be updated by simply moving a file to a different directory. In this example, it would change the year value as a result of being in a different directory.
Yes, it's strange, but the trick is to stop thinking of it like a normal database and appreciate the freedom that it offers. For example, appending new data is as simple as dropping a file into a directory. No loading required!
I have exported a mysql table to a parquet file (avro based). Now i want to read particular columns from that file. How can i read particular columns completely? I am looking for java code examples.
Is there an api where i can pass the columns i need and get back a 2D array of table?
If you can use hive, creating a hive table and issuing a simple select query would be by far the easiest option.
create external table tbl1(<columns>) location '<file_path>' stored as parquet;
select col1,col2 from tbl1;
//this works in hive 0.14
You can use JDBC driver to do that from java program as well.
Otherwise, if you want to stay completely in java, you need to modify the avro schema by excluding all the fields but the ones you want to fetch. Then when you read the file supply the modified schema as reader schema and it will only read the included columns. But you will get you original avro record back with excluded fields nullified, not a 2D array.
To modify the schema look at org.apache.avro.Schema and org.apache.avro.SchemaBuilder. make sure that modified schema is compatible with the original schema.
Options:
Use Hive table to create table with all columns with storage format parquet and read the required columns by specifying the column names
Create Thrift for the table and use the thrift fields to read the data from code (Java or Scala)
You can also use apache drill that natively parse parquet files.