Using Text Data Files

Impala supports using text files as the storage format for input and output. Text files are a convenient format to use for interchange with other applications or scripts that produce or read delimited text files, such as CSV or TSV with commas or tabs for delimiters.

Text files are flexible in their column definitions. For example, a text file could have more fields than the Impala table, and those extra fields are ignored during queries. Or it could have fewer fields than the Impala table, and those missing fields are treated as NULL values in queries.

You could have fields that were treated as numbers or timestamps in a table, then use ALTER TABLE ... REPLACE COLUMNS to switch them to strings, or the reverse.

Creating Text Tables

You can create tables with specific separator characters to import text files in familiar formats such as CSV, TSV, or pipe-separated with the FIELDS TERMINATED BY clause preceded by the ROW FORMAT DELIMITED clause. For example:


CREATE TABLE tsv(id INT, s STRING, n INT, t TIMESTAMP, b BOOLEAN)
  ROW FORMAT DELIMITED
  FIELDS TERMINATED BY '\t';

You can specify a delimiter character '\0' to use the ASCII 0 (nul) character for text tables.

You can also use these tables to produce output data files, by copying data into them through the INSERT ... SELECT syntax and then extracting the data files from the Impala data directory.

The data files created by any INSERT statements uses the Ctrl-A character (hex 01) as a separator between each column value.

Issue a DESCRIBE FORMATTED table_name statement to see the details of how each table is represented internally in Impala.

Complex type considerations: Although you can create tables in this file format using the complex types (ARRAY, STRUCT, and MAP), currently, Impala cannot query these types in text tables.

Data Files for Text Tables

When Impala queries a table with data in text format, it consults all the data files in the data directory for that table, with some exceptions:

Impala ignores any hidden files, that is, files whose names start with a dot or an underscore.
Impala queries ignore files with extensions commonly used for temporary work files by Hadoop tools. Any files with extensions .tmp or .copying are not considered part of the Impala table. The suffix matching is case-insensitive, so for example Impala ignores both .copying and .COPYING suffixes.
Impala uses suffixes to recognize when text data files are compressed text. For Impala to recognize the compressed text files, they must have the appropriate file extension corresponding to the compression codec, either .bz2, .gz, .snappy, or .zst, .deflate. The extensions can be in uppercase or lowercase.
Otherwise, the file names are not significant. When you put files into an HDFS directory through ETL jobs, or point Impala to an existing HDFS directory with the CREATE EXTERNAL TABLE statement, or move data files under external control with the LOAD DATA statement, Impala preserves the original filenames.

An INSERT ... SELECT statement produces one data file from each node that processes the SELECT part of the statement. An INSERT ... VALUES statement produces a separate data file for each statement; because Impala is more efficient querying a small number of huge files than a large number of tiny files, the INSERT ... VALUES syntax is not recommended for loading a substantial volume of data. If you find yourself with a table that is inefficient due to too many small data files, reorganize the data into a few large files by doing INSERT ... SELECT to transfer the data to a new table.

Do not surround string values with quotation marks in text data files that you construct. If you need to include the separator character inside a field value, for example to put a string value with a comma inside a CSV-format data file, specify an escape character on the CREATE TABLE statement with the ESCAPED BY clause, and insert that character immediately before any separator characters that need escaping.

Special values within text data files:

Impala recognizes the literal strings inf for infinity and nan for Not a Number, for FLOAT and DOUBLE columns.
Impala recognizes the literal string \N to represent NULL. When using Sqoop, specify the options --null-non-string and --null-string to ensure all NULL values are represented correctly in the Sqoop output files. \N needs to be escaped as in the below example:
```
--null-string '\\N' --null-non-string '\\N'
```
By default, Sqoop writes NULL values using the string null, which causes a conversion error when such rows are evaluated by Impala. (A workaround for existing tables and data files is to change the table properties through ALTER TABLE name SET TBLPROPERTIES("serialization.null.format"="null").)
Impala can optionally skip an arbitrary number of header lines from text input files on HDFS based on the skip.header.line.count value in the TBLPROPERTIES field of the table metadata.

Loading Data into Text Tables

To load an existing text file into an Impala text table, use the LOAD DATA statement and specify the path of the file in HDFS. That file is moved into the appropriate Impala data directory.

To load multiple existing text files into an Impala text table, use the LOAD DATA statement and specify the HDFS path of the directory containing the files. All non-hidden files are moved into the appropriate Impala data directory.

Use the DESCRIBE FORMATTED statement to see the HDFS directory where the data files are stored, then use Linux commands such as hdfs dfs -ls hdfs_directory and hdfs dfs -cat hdfs_file to display the contents of an Impala-created text file.

When you create a text file for use with an Impala text table, specify \N to represent a NULL value.

If a text file has fewer fields than the columns in the corresponding Impala table, all the corresponding columns are set to NULL when the data in that file is read by an Impala query.

If a text file has more fields than the columns in the corresponding Impala table, the extra fields are ignored when the data in that file is read by an Impala query.

You can also use manual HDFS operations such as hdfs dfs -put or hdfs dfs -cp to put data files in the data directory for an Impala table. When you copy or move new data files into the HDFS directory for the Impala table, issue a REFRESH table_name statement in impala-shell before issuing the next query against that table, to make Impala recognize the newly added files.

Query Performance for Text Tables

Data stored in text format is relatively bulky, and not as efficient to query as binary formats such as Parquet. For the tables used in your most performance-critical queries, look into using more efficient alternate file formats.

For frequently queried data, you might load the original text data files into one Impala table, then use an INSERT statement to transfer the data to another table that uses the Parquet file format. The data is converted automatically as it is stored in the destination table.

For more compact data, consider using LZO compression for the text files. The splittable nature of LZO data files lets different nodes work on different parts of the same file in parallel.

You can also use text data compressed in the gzip, bzip2, or Snappy formats. However, because these compressed formats are not splittable in the way that LZO is, there is less opportunity for Impala to parallelize queries on them. Therefore, use these types of compressed data only for convenience if that is the format in which you receive the data. Prefer to use LZO compression for text data if you have the choice, or convert the data to Parquet using an INSERT ... SELECT statement to copy the original data into a Parquet table.

Creating LZO Compressed Text Tables

Impala supports using text data files that employ LZO compression. Cloudera recommends compressing text data files when practical. Impala queries are usually I/O-bound; reducing the amount of data read from disk typically speeds up a query, despite the extra CPU work to uncompress the data in memory.

Impala can work with LZO-compressed text files. LZO-compressed files are preferable to text files compressed by other codecs, because LZO-compressed files are splittable, meaning that different portions of a file can be uncompressed and processed independently by different nodes.

Because Impala can query LZO-compressed files but currently cannot write them, you use Hive to do the initial CREATE TABLE and load the data, then switch back to Impala to run queries. Once you have created an LZO text table, you can manually add LZO-compressed text files to it.

A table containing LZO-compressed text files must be created in Hive with the following storage clause:

STORED AS
    INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
    OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'

Also, certain Hive settings need to be in effect. For example:

hive> SET mapreduce.output.fileoutputformat.compress=true;
hive> SET hive.exec.compress.output=true;
hive> SET mapreduce.output.fileoutputformat.compress.codec=com.hadoop.compression.lzo.LzopCodec;
hive> CREATE TABLE lzo_t (s string) STORED AS
  > INPUTFORMAT 'com.hadoop.mapred.DeprecatedLzoTextInputFormat'
  > OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat';
hive> INSERT INTO TABLE lzo_t SELECT col1, col2 FROM uncompressed_text_table;

Once you have created LZO-compressed text tables, you can convert data stored in other tables (regardless of file format) by using the INSERT ... SELECT statement in Hive.

Files in an LZO-compressed table must use the .lzo extension. Examine the files in the HDFS data directory after doing the INSERT in Hive, to make sure the files have the right extension. If the required settings are not in place, you end up with regular uncompressed files, and Impala cannot access the table because it finds data files with the wrong (uncompressed) format.

After loading data into an LZO-compressed text table, index the files so that they can be split. You index the files by running a Java class, com.hadoop.compression.lzo.DistributedLzoIndexer, through the Linux command line. This Java class is included in the hadoop-lzo package.

Run the indexer using a command like the following:

$ hadoop jar /usr/lib/hadoop/lib/hadoop-lzo-version-gplextras.jar
  com.hadoop.compression.lzo.DistributedLzoIndexer /hdfs_location_of_table/

Indexed files have the same name as the file they index, with the .index extension. If the data files are not indexed, Impala queries still work, but the queries read the data from remote DataNodes, which is very inefficient.

Once the LZO-compressed tables are created, and data is loaded and indexed, you can query them through Impala. As always, the first time you start impala-shell after creating a table in Hive, issue an INVALIDATE METADATA statement so that Impala recognizes the new table. (In Impala 1.2 and higher, you only have to run INVALIDATE METADATA on one node, rather than on all the Impala nodes.)

Using bzip2, deflate, gzip, Snappy-Compressed, or zstd Text Files

Impala supports using text data files that employ bzip2, deflate, gzip, Snappy, or zstd compression. These compression types are primarily for convenience within an existing ETL pipeline rather than maximum performance. Although it requires less I/O to read compressed text than the equivalent uncompressed text, files compressed by these codecs are not splittable and therefore cannot take full advantage of the Impala parallel query capability. Impala can read compressed text files written by Hive.

As each Snappy-compressed file is processed, the node doing the work reads the entire file into memory and then decompresses it. Therefore, the node must have enough memory to hold both the compressed and uncompressed data from the text file. The memory required to hold the uncompressed data is difficult to estimate in advance, potentially causing problems on systems with low memory limits or with resource management enabled. This memory overhead is reduced for bzip2-, deflate-, gzip-, and zstd-compressed text files. The compressed data is decompressed as it is read, rather than all at once.

To create a table to hold compressed text, create a text table with no special compression options. Specify the delimiter and escape character if required, using the ROW FORMAT clause.

Because Impala can query compressed text files but currently cannot write them, produce the compressed text files outside Impala and use the LOAD DATA statement, manual HDFS commands to move them to the appropriate Impala data directory. (Or, you can use CREATE EXTERNAL TABLE and point the LOCATION attribute at a directory containing existing compressed text files.)