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
.snappy. 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
nanfor “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
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
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
... SELECT statement to copy the original data into a Parquet
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
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
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,
through the Linux command line. This Java class is included in the
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 gzip, bzip2, or Snappy-Compressed Text Files
Impala supports using text data files that employ gzip, bzip2, or Snappy 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.
As each bzip2- or Snappy-compressed text 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. In Impala 2.1 and higher, this memory overhead is reduced for gzip-compressed text files. The gzipped data is decompressed as it is read, rather than all at once.
To create a table to hold gzip, bzip2, or Snappy-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.)
For Impala to recognize the compressed text files, they must have the
appropriate file extension corresponding to the compression codec,
.snappy. The extensions can be in uppercase or