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
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
FIELDS TERMINATED BYclause preceded by the
ROW FORMAT DELIMITEDclause. 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.
INSERT ... SELECTsyntax and then extracting the data files from the Impala data directory.
The data files created
INSERT statements uses the Ctrl-A character (hex
01) as a separator between each column value.
DESCRIBE FORMATTED table_name
statement to see the details of how each table is represented internally
Complex type considerations: Although you can create tables in
this file format using the complex types (
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
.copyingare not considered part of the Impala table. The suffix matching is case-insensitive, so for example Impala ignores both
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
.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 TABLEstatement, or move data files under external control with the
LOAD DATAstatement, Impala preserves the original filenames.
INSERT ... SELECT statement produces one data file
from each node that processes the
SELECT part of the
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,
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
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
inffor infinity and
Not a Number, for
Impala recognizes the literal string
NULL. When using Sqoop, specify the options
--null-stringto ensure all
NULLvalues are represented correctly in the Sqoop output files.
\Nneeds to be escaped as in the below example:
--null-string '\\N' --null-non-string '\\N'
By default, Sqoop writes
NULLvalues 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.countvalue in the
TBLPROPERTIESfield 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
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.
DESCRIBE FORMATTED statement to see the HDFS
directory where the data files are stored, then use Linux commands such
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
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
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 -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 text data compressed in the gzip, bzip2, or Snappy
formats. However, these compressed formats are not
splittable so there is less
opportunity for Impala to parallelize queries on them. You also have the choice to convert
the data to Parquet using an
INSERT ... SELECT statement to copy the
original data into a Parquet table.
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
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.)