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LOAD DATA

Insert data stored in a CSV, JSON, or Avro file into a table.

CSV LOAD DATA

Syntax

LOAD DATA [LOCAL] INFILE 'file_name'
  [REPLACE | IGNORE | SKIP { ALL | CONSTRAINT | DUPLICATE KEY } ERRORS]
  INTO TABLE tbl_name
  [CHARACTER SET charset_name]
  [{FIELDS | COLUMNS}
  [TERMINATED BY 'string']
      [[OPTIONALLY] ENCLOSED BY 'char']
      [ESCAPED BY 'char']
  ]
  [LINES
  [STARTING BY 'string']
  [TERMINATED BY 'string']
  ]
  [TRAILING NULLCOLS]
  [NULL DEFINED BY string_to_insert_as_null]
  [IGNORE number LINES]
  [ ({col_name | @variable_name}, ...) ]
  [SET col_name = expr,...]
  [WHERE expr,...]
  [MAX_ERRORS number]
  [ERRORS HANDLE string]

Remarks

Error logging and error handling are discussed at the end of this topic.

ON DUPLICATE KEY UPDATE allows you to perform an upsert of a row. For more information, see the Performing Upserts guide.

If no FIELDS or LINES clause is specified, then MemSQL uses the following defaults:

FIELDS TERMINATED BY '\t'
ENCLOSED BY ''
ESCAPED BY '\\'
LINES TERMINATED BY '\n'
STARTING BY ''

To specify the compression type of an input file, use the COMPRESSION clause. See Handling Data Compression for more information.

The ESCAPED BY clause allows you to specify the escape character. For example, if the input data contains special character(s), you may need to escape those characters to avoid misinterpretation. Also, you may need to redefine the default escape character to load a data set that contains the said character.

The SET clause allows you to set columns using specific values or expressions with user defined variables. For example, if your input file has 9 columns but the table has a 10th column called foo, you can add SET foo=0 or SET foo=@myVariable. Note that column names may only be used on the left side of SET expressions.

The WHERE clause allows you to do filtering on incoming data. Only rows that satisfy the expression in the WHERE clause will be loaded into MemSQL. For an example of how to use the WHERE clause, see the examples section below.

The TRAILING NULLCOLS clause allows the input file to contain rows having fewer than the number of columns in the table. These missing fields must be trailing columns in the row; they are inserted as NULL values in the table.

The NULL DEFINED BY clause inserts NULL field values in the table for fields in the input file having the value string_to_insert_as_null.

The behavior of MemSQL’s LOAD DATA command has several functional differences from MySQL’s command:

  • LOAD DATA will load the data into MemSQL in parallel to maximize performance. This makes LOAD DATA in MemSQL much faster on machines with a larger number of processors.
  • LOAD DATA supports loading compressed .gz files.
  • The only supported charset_name is utf8.
  • While it is possible to execute LOAD DATA on leaf nodes, it should only be run on master aggregator or child aggregator node types. See Node Requirements for MemSQL Commands for more information. Note that when running this command on reference tables you must connect to the master aggregator.

The mysqlimport utility can also be used to import data into MemSQL. mysqlimport uses LOAD DATA internally.

MemSQL stores information about errors encountered during each LOAD DATA operation, but the number of errors is limited to 1000 by default. When this limit is reached, the load fails. This prevents out-of-memory issues when unintentionally loading large files with incorrect format or an incorrect LOAD DATA statement. Use MAX_ERRORS at the end of the statement to change this limit.

Writing to multiple databases in a transaction is not supported.

Examples

Example 1

If the order of columns in the table is different from the order in the source file, you can name them explicitly. In this example, the columns are loaded in reverse order:

LOAD DATA INFILE 'foo.tsv'
  INTO TABLE foo (fourth, third, second, first);

Example 2

You can also skip columns in the source file using the “@” sign. In this example only the first and fourth columns are imported into table foo:

LOAD DATA INFILE 'foo.tsv'
  INTO TABLE foo (bar, @, @, baz);

Example 3

The default column delimiter is the tab (“t”) character, ASCII code 09. You can specify a different delimiter, even multi-character delimiters, with the COLUMNS TERMINATED BY clause:

LOAD DATA INFILE 'foo.csv'
  INTO TABLE foo
  COLUMNS TERMINATED BY ',';

Example 4

The following example demonstrates loading a file that has unusual column separators (|||):

LOAD DATA INFILE 'foo.oddformat'
  INTO TABLE foo
  COLUMNS TERMINATED BY '|||';

Example 5

You can also filter out unwanted rows using the WHERE clause. In this example only rows where bar is equal to 5 will be loaded. All other rows will be discarded:

LOAD DATA INFILE 'foo.oddformat'
  INTO TABLE foo (bar, baz)
  WHERE bar = 5;

Example 6

Complex transformations can be performed in both the ‘SET’ and ‘WHERE’ clauses. For example, if you have an input file with a EventDate field and an EventId field:

10-1-2016,1
4-15-2016,2
1-10-2017,3
4-10-2017,4

You want to only load the rows with a date that is within three months from a certain date, 10/15/2016, for instance. This can be accomplished by the following:

CREATE TABLE foo (EventDate date, EventId int);

LOAD DATA INFILE 'date_event.csv'
  INTO TABLE foo
  FIELDS TERMINATED BY ','
  (@EventDate, EventId)
  SET EventDate = STR_TO_DATE(@EventDate, '%m-%d-%Y')
  WHERE ABS(MONTHS_BETWEEN(EventDate, date('2016-10-15'))) < 3;

SELECT * FROM t;
****
+------------+---------+
| EventDate  | EventId |
+------------+---------+
| 2016-10-01 |       1 |
| 2017-01-10 |       3 |
+------------+---------+

While both column names and variables can be referenced in the WHERE clause column names can only be assigned to in the SET clause. The scope of these clauses is restricted to the current row and therefore SELECT statements cannot be evaluated.

Example 7

The following example demonstrates how to use the TRAILING NULLCOLS clause using the file numbers.csv , whose contents are shown below.

1,2,3
4,5
6

Run the following commands:

CREATE TABLE foo(a INT, b INT, c INT);

LOAD DATA INFILE 'numbers.csv' INTO TABLE foo COLUMNS TERMINATED BY ',' TRAILING NULLCOLS;

SELECT * FROM foo;
****
+------+------+------+
| a    | b    | c    |
+------+------+------+
|    1 |    2 |    3 |
|    4 |    5 | NULL |
|    6 | NULL | NULL |
+------+------+------+

Example 8

The following example demonstrates how to use the NULL DEFINED BY clause using the file numbers2.csv , whose contents are shown below.

1,2,3
,10,
50,,

Run the following commands:

CREATE TABLE foo(a INT, b INT, c INT);

LOAD DATA INFILE 'numbers2.csv' INTO TABLE foo COLUMNS TERMINATED BY ',' NULL DEFINED BY '';

SELECT * FROM foo;
****
+------+------+------+
| a    | b    | c    |
+------+------+------+
|    1 |    2 |    3 |
| NULL |   10 | NULL |
|   50 | NULL | NULL |
+------+------+------+

Example 9

The following example demonstrates how to load data into the loadEsc table using the ESCAPED BY clause from the file contacts.csv, whose contents are shown below.

GALE\, ADAM, Brooklyn
FLETCHER\, RON, New York
WAKEFIELD\, CLARA, DC
DESC loadEsc;
****
+-------+-------------+------+------+---------+-------+
| Field | Type        | Null | Key  | Default | Extra |
+-------+-------------+------+------+---------+-------+
| Name  | varchar(40) | YES  |      | NULL    |       |
| City  | varchar(40) | YES  |      | NULL    |       |
+-------+-------------+------+------+---------+-------+

Execute the following query:

LOAD DATA INFILE '/contacts.csv'
INTO TABLE loadEsc COLUMNS TERMINATED BY ',' ESCAPED BY '\\' ;
SELECT * FROM loadEsc;
****
+-------------------+-----------+
| Name              | City      |
+-------------------+-----------+
| GALE, ADAM        |  Brooklyn |
| FLETCHER, RON     |  New York |
| WAKEFIELD, CLARA  |  DC       |
+-------------------+-----------+

In this query, the \ character escapes the comma (,) between the first two fields of the contacts.csv file. (The \ (backslash) is the default escape character in a SQL query. Hence, the \\ (double backslash) is used escape the backslash itself inside the query.)

Warning

If you (accidentally) escape the TERMINATED BY character in a file, the SQL query may return an error. For example, if you escape both the commas in any row of the contacts.csv file mentioned above, as:

GALE\, ADAM\, Brooklyn
FLETCHER\, RON, New York
WAKEFIELD\, CLARA, DC

and then execute the following query

LOAD DATA INFILE '/contacts.csv'
INTO TABLE loadEsc COLUMNS TERMINATED BY ',' ESCAPED BY '\\' ;

it returns the following error: ERROR 1261 (01000): Row 1 doesn’t contain data for all columns. Because, the \ (backslash) escapes both the commas and LOAD DATA perceives the first row as a single column.

JSON LOAD DATA

Syntax

LOAD DATA [LOCAL] INFILE 'file_name'
  [REPLACE | SKIP { CONSTRAINT | DUPLICATE KEY } ERRORS]
  INTO TABLE tbl_name
  FORMAT JSON
  subvalue_mapping
  [SET col_name = expr,...]
  [WHERE expr,...]
  [MAX_ERRORS number]
  [ERRORS HANDLE string]

subvalue_mapping:
  ( {col_name | @variable_name} <- subvalue_path [DEFAULT literal_expr], ...)

subvalue_path:
  {% | [%::]ident [::ident ...]}

Semantics

Error logging and error handling are discussed at the end of this topic.

Extract specified subvalues from each JSON value in file_name. Assign them to specified columns of a new row in tbl_name, or to variables used for a column assignment in a SET clause. If a specified subvalue can’t be found in an input JSON, assign the DEFAULT clause literal instead. Discard rows which don’t match the WHERE clause.

To specify the compression type of an input file, use the COMPRESSION clause. See Handling Data Compression for more information.

The file named by file_name must consist of concatenated UTF-8 encoded JSON values, optionally separated by whitespace. Newline-delimited JSON is accepted, for example.

Non-standard JSON values like NaN, Infinity, and -Infinity must not occur in file_name.

If file_name ends in .gz or .lz4, it will be decompressed.

JSON LOAD DATA supports a subset of the error recovery options allowed by CSV LOAD DATA. Their behavior is as described under CSV LOAD DATA.

Writing to multiple databases in a transaction is not supported.

Extracting JSON Values

subvalue_mapping specifies which subvalues are extracted and the column or variable to which each one is assigned.

LOAD DATA uses the ::-separated list of keys in a subvalue_path to perform successive key lookups in nested JSON objects, as if applying the :: SQL operator. Unlike the :: operator, subvalue_path may not be used to extract an element of a JSON array. The path % refers to the entire JSON value being processed. Leading %:: may be omitted from paths which are otherwise non-empty.

If a path can’t be found in an input JSON value, then if the containing element of subvalue_mapping has a DEFAULT clause, its literal_expr will be assigned; otherwise, LOAD DATA will terminate with an error.

Path components containing whitespace or punctuation must be surrounded by backticks. For example, the paths %::`a.a`::b and `a.a`::b will both extract 1 from the input object {"a.a":{"b":1},"c":2}.

Array elements may be indirectly extracted by applying JSON_EXTRACT_<type> in a SET clause.

Converting JSON Values

Before assignment or set clause evaluation, the JSON value extracted according to a subvalue_path is converted to a binary collation SQL string whose value depends on the extracted JSON type as follows:

JSON Type Converted Value
null SQL NULL
true/false "1"/"0"
number Verbatim, from extracted string.
string All JSON string escape sequences, including \u-escape sequences are converted to UTF-8. Verbatim otherwise.
array Verbatim, from extracted string. For example, '[1,2]'
object Verbatim, from extracted string. For example, '{"k":true}'

Conversion isn’t recusive. So, for example, true isn’t converted to "1" when it’s a subvalue of an object which is being extracted whole.

Examples

Example 1

If example.json consists of:

{"a":{"b":1}, "c":null}
{"a":{"b":2}, "d":null}

Then it can be loaded as follows:

CREATE TABLE t(a INT);

LOAD DATA LOCAL INFILE "example.json" INTO TABLE t(a <- a::b) FORMAT JSON;

SELECT * FROM t;
****
+------+
| a    |
+------+
|    1 |
|    2 |
+------+

Example 2

If example2.json consists of:

{"b":true, "s":"A\u00AE\u0022A", "n":-1.4820790816978637e-25, "a":[1,2], "o":{"subobject":1}}
{"b":false}
"hello"

Then we can perform a more complicated LOAD DATA:

CREATE TABLE t(b bool NOT NULL, s TEXT, n DOUBLE, a INT, o JSON NOT NULL, whole longblob);

LOAD DATA LOCAL INFILE "example2.json" INTO TABLE t FORMAT JSON(
  b <- b default true,
  s <- s default NULL,
  n <- n default NULL,
  @avar <- a default NULL,
  o <- o default '{"subobject":"replaced"}',
  whole <- %)
  SET a = json_extract_double(@avar, 1)
  WHERE b = true;

SELECT * FROM t;
****
+---+-------+-------------------------+------+--------------------------+-----------------------------------------------------------------------------------------------+
| b | s     | n                       | a    | o                        | whole                                                                                         |
+---+-------+-------------------------+------+--------------------------+-----------------------------------------------------------------------------------------------+
| 1 | A®"A  | -1.4820790816978637e-25 |    2 | {"subobject":1}          | {"b":true, "s":"A\u00AE\u0022A", "n":-1.4820790816978637e-25, "a":[1,2], "o":{"subobject":1}} |
| 1 | NULL  |                    NULL | NULL | {"subobject":"replaced"} | hello                                                                                         |
+---+-------+-------------------------+------+--------------------------+-----------------------------------------------------------------------------------------------+

There are several things to note in the example above:

  • true was converted to "1" for columns b, but not for column whole. "1" was further converted to the BOOL value 1.
  • The \u-escapes "\u00AE" and "\u0022" were converted to UTF-8 for column s, but not for column whole. Note that whole would have become invalid JSON if we had translated "\u0022".
  • The second row was discarded because it failed to match the WHERE clause.
  • None of the paths in subvalue_mapping could be found in the third row, so DEFAULT literals like '{"subobject":"replaced"}' were assigned instead.
  • We assigned a to an intermediate variable so that we could extract an array element in the SET clause.
  • The “top-level” JSON values in example2.json weren’t all JSON objects. "hello" is a valid “top-level” JSON value.

Avro LOAD DATA

Syntax

LOAD DATA [LOCAL] INFILE 'file_name'
  [REPLACE | SKIP { CONSTRAINT | DUPLICATE KEY } ERRORS]
  INTO TABLE tbl_name
  FORMAT AVRO
  subvalue_mapping
  [SET col_name = expr,...]
  [WHERE expr,...]
  [MAX_ERRORS number]
  [ERRORS HANDLE string]
  [SCHEMA 'avro_schema']

subvalue_mapping:
  ( {col_name | @variable_name} <- subvalue_path, ...)

subvalue_path:
  {% | [%::]ident [::ident ...]}

Semantics

Error logging and error handling are discussed at the end of this topic.

Extract specified subvalues from each Avro value in file_name. Assign them to specified columns of a new row in tbl_name, or to variables used for a column assignment in a SET clause. Discard rows which don’t match the WHERE clause.

To specify the compression type of an input file, use the COMPRESSION clause. See Handling Data Compression for more information.

Avro LOAD DATA expects Avro data in one of two “sub-formats”, depending on the SCHEMA clause.

If no SCHEMA clause is provided, file_name must name an Avro Object Container File as described in version 1.8.2 of the Avro specification. In addition, the following restrictions hold:

  • The compression codec of the file must be null.

  • Array and map values must not have more than 16384 elements.

  • The type name of a record must not be used in a symbolic “reference to previously defined name” in any of its fields. It may still be used in a symbolic reference outside the record definition, however.

    For example, self-referential schemas like the following are rejected by LOAD DATA:

    {
      "type": "record",
      "name": "PseudoLinkedList",
      "fields" : [{"name": "value", "type": "long"},
                  {"name": "next", "type": ["null", "PseudoLinkedList"]}]
    }
    

If a SCHEMA clause is provided, the file must be a “raw stream” consisting of only the concatenated binary encodings of instances of avro_schema. avro_schema must be a SQL string containing a JSON Avro schema. The restrictions on Object Container Files also apply to “raw stream” files.

Warning

It’s an error to provide a SCHEMA clause when loading an Object Container File because it contains metadata alongside the encoded values.

All optional Avro schema attributes except the namespace attribute are ignored. Notably, logicalType attributes are ignored.

If file_name ends in .gz or .lz4, it will be decompressed.

Avro LOAD DATA supports a subset of the error recovery options allowed by CSV LOAD DATA. Their behavior is as described under CSV LOAD DATA.

Writing to multiple databases in a transaction is not supported.

Extracting Avro Values

subvalue_mapping specifies which subvalues are extracted and the column or variable to which each one is assigned.

LOAD DATA uses the ::-separated list of names in a subvalue_path to perform successive field name or union branch type name lookups in nested Avro records or unions. subvalue_path may not be used to extract elements of Avro arrays or maps. The path % refers to the entire Avro value being processed. Leading %:: may be omitted from paths which are otherwise non-empty.

If a path can’t be found in an input Avro value, then:

  • If a prefix of the path matches a record whose schema has no field matching the next name in the path, then LOAD DATA will terminate with an error.
  • If a prefix matches a union whose schema has no branch matching the next name, then LOAD DATA will terminate terminate with an error.
  • If a prefix matches a union whose schema has a branch matching the next name, but that branch isn’t the selected branch in that instance of the union schema, then Avro null will be extracted instead and LOAD DATA will continue.

Path components naming union branches must use the two-part fullname of the branch’s type if that type is in a namespace.

Path components containing whitespace or punctuation must be surrounded by backticks.

Array and map elements may be indirectly extracted by applying JSON_EXTRACT_<type> in a SET clause.

For example, consider two Avro records with the union schema:

[
    "int",
    { "type" : "record",
      "name" : "a",
      "namespace" : "n",
      "fields" : [{ "name" : "f1",
                    "type" : "int" }]
    }
]

The paths %::`n.a`::f1 and `n.a`::f1 will both extract 1 from an instance of this schema whose JSON encoding is {"n.a":{"f1":1}}.

They will extract null from an instance whose encoding is {"int":2}.

The paths %::int and int will extract 2 from the second instance and null from the first.

Converting Avro Values

Before assignment or set clause evaluation, the Avro value extracted according to a subvalue_path is converted to an unspecified SQL type which may be further explicitly or implicitly converted as if from a SQL string whose value is as follows:

Avro Type Converted Value
null SQL NULL
boolean "1"/"0"
int The string representation of the value
long The string representation of the value
float SQL NULL if not finite. Otherwise, a string convertible without loss of precision to FLOAT
double SQL NULL if not finite. Otherwise, a string convertible without loss of precision to DOUBLE
enum The string representation of the enum.
bytes Verbatim, from input bytes
string Verbatim, from input bytes
fixed Verbatim, from input bytes
record The JSON encoding of the value
map The JSON encoding of the value
array The JSON encoding of the value
union The JSON encoding of the value

logicalType attributes are ignored and have no effect on conversion.

Examples

Example 1

Consider an Avro Object Container File example.avro with the following schema:

{
  "type": "record",
  "name": "data",
  "fields": [{ "name": "id", "type": "long"},
             { "name": "payload", "type": [ "null",
                                            "string" ]}]
}

example.avro contains three Avro values whose JSON encodings are:

{"id":1,"payload":{"string":"first"}}
{"id":1,"payload":{"string":"second"}}
{"id":1,"payload":null}

example.avro can be loaded as follows:

CREATE TABLE t(payload TEXT, input_record JSON);

LOAD DATA LOCAL INFILE "example.avro"
  INTO TABLE t
  FORMAT AVRO
  ( payload <- %::payload::string,
  input_record <- % );

SELECT * FROM t;
****
+---------+----------------------------------------+
| payload | input_record                           |
+---------+----------------------------------------+
| first   | {"id":1,"payload":{"string":"first"}}  |
| second  | {"id":1,"payload":{"string":"second"}} |
| NULL    | {"id":1,"payload":null}                |
+---------+----------------------------------------+

LOAD DATA was able to parse example.avro because Avro Object Container Files have a header which contains their schema.

Example 2

Consider a file named example.raw_avro, with the same values as example.avro from Example 1 but in the “raw stream format”. That is, example.raw_avro consists of the binary encoded values and nothing else. We add a SCHEMA clause to tell LOAD DATA to expect a “raw stream” with the provided schema:

CREATE TABLE t(payload TEXT, input_record JSON);
LOAD DATA LOCAL INFILE "example.raw_avro"
  INTO TABLE t
  FORMAT AVRO
  ( payload <- %::payload::string,
  input_record <- % )
  schema
  '{
    "type": "record",
    "name": "data",
    "fields": [{ "name": "id", "type": "long"},
      { "name": "payload", "type": [ "null", "string" ]}]
  }';

SELECT * FROM t;
****
+---------+----------------------------------------+
| payload | input_record                           |
+---------+----------------------------------------+
| first   | {"id":1,"payload":{"string":"first"}}  |
| second  | {"id":1,"payload":{"string":"second"}} |
| NULL    | {"id":1,"payload":null}                |
+---------+----------------------------------------+

Example 3

Consider an Object Container File example3.avro with a more complicated payload than Example 1. We illustrate extracting values from nested unions and records, and also indirectly extracting elements of nested maps and arrays.

{ "type": "record",
  "namespace": "ns",
  "name": "data",
  "fields": [
      { "name": "id", "type": "long" },
      { "name": "payload", "type":
        [ "null",
          { "type": "record",
            "name": "payload_record",
            "namespace": "ns",
            "fields": [
                { "name": "f_bytes", "type": "bytes"},
                { "name": "f_string", "type": "string"},
                { "name": "f_map", "type":
                  { "type": "map",
                    "values": { "type": "array",
                                "items": "int" }}}
            ]
          }
        ]
      }
  ]
}

The raw JSON encoding of the contents of this file can be seen in column c_whole_raw after the following LOAD DATA:

CREATE TABLE t (
  c_id bigint,
  c_bytes longblob,
  c_string longblob,
  c_array_second int,
  c_whole_raw longblob,
  c_whole_json json
  );

LOAD DATA INFILE "example3.avro"
  INTO TABLE t
  FORMAT AVRO
  ( c_id <- %::id,
  c_bytes <- %::payload::`ns.payload_record`::f_bytes,
  c_string <- %::payload::`ns.payload_record`::f_string,
  @v_map <- %::payload::`ns.payload_record`::f_map,
  c_whole_raw <- %,
  c_whole_json <- %)
  SET c_array_second = JSON_EXTRACT_JSON(@v_map, "a", 1);

SELECT * FROM t;
****
*************************** 1. row ***************************
          c_id: 1
       c_bytes: NULL
      c_string: NULL
c_array_second: NULL
   c_whole_raw: {"id":1,"payload":null}
  c_whole_json: {"id":1,"payload":null}
*************************** 2. row ***************************
          c_id: 2
       c_bytes: "A
      c_string: "A
c_array_second: 2
   c_whole_raw: {"id":2,"payload":{"ns.payload_record":{"f_bytes":"\u0022\u0041","f_string":"\"A","f_map":{"a":[1,2]}}}}
  c_whole_json: {"id":2,"payload":{"ns.payload_record":{"f_bytes":"\"A","f_map":{"a":[1,2]},"f_string":"\"A"}}}

There are several things to note:

  • We attempted to extract subvalues of the payload_record branch of the union-type payload field. Since that wasn’t the selected member of the union in record 1, LOAD DATA assigned NULL to c_bytes and @v_map.
  • We assigned the JSON encoding of f_map to @v_map and then performed JSON map and array lookups in the SET clause to ultimately extract 2.
  • f_string and f_bytes had the same contents, but we can see how their different Avro types affected their JSON encodings and interacted with the SQL JSON type:
    • The JSON encoding of the Avro string value f_string, as seen in c_whole_raw, encodes special characters like " as the escape sequence \".
    • The JSON encoding of the Avro bytes value f_bytes, as seen in c_whole_raw, encodes every byte with a JSON \u-escape.
    • When converting the JSON encoding of record 2 to the SQL JSON type while assigning to c_whole_json, LOAD DATA normalized both representations of the byte sequence "A to \"A.

Loading Parquet Files

The LOAD DATA command does not support loading Parquet files. However, you can use the LOAD DATA clause in a CREATE PIPELINE .. FORMAT PARQUET statement to create a pipeline that loads Parquet files.

Handling Data Compression

The COMPRESSION clause specifies how LOAD DATA handles the compression of an input file.

Syntax

LOAD DATA INFILE 'filename' COMPRESSION { AUTO | NONE | LZ4 | GZIP } INTO TABLE ...

LOAD DATA INFILE 'filename' INTO TABLE `tablename` COMPRESSION { AUTO | NONE | LZ4 | GZIP } ...

Arguments

  • AUTO: This is the default setting, it tells LOAD DATA to identify the compression type from the input file’s extension.
  • NONE: Specifies that the input file is uncompressed.
  • LZ4: Specifies that the input file is compressed with LZ4 compression algorithm.
  • GZIP: Specifies that the input file is compressed with GZIP compression algorithm.

Remarks

  • If COMPRESSION is set to NONE, LZ4, or GZIP, LOAD DATA will not use the extension of the input file to determine the type of compression. For example, if you load a file test.gz and specify the COMPRESSION as NONE, then LOAD DATA will handle test.gz as an uncompressed file.

Error Logging

When you run the LOAD DATA command and use the ERRORS HANDLE clause, LOAD DATA logs errors to the information_schema.LOAD_DATA_ERRORS table. The logged errors are the erroneous lines that LOAD DATA encountered as it processed the input file. The following table describes information_schema.LOAD_DATA_ERRORS.

Column Name Description
DATABASE_NAME The name of the database associated with the error.
HANDLE The string value specified in the LOAD DATA ... ERRORS HANDLE <string> statement that caused the error.
ERROR_UNIX_TIMESTAMP The time of the error event in Unix timestamp format.
ERROR_TYPE Specifies what type of error occurred. Error is used when LOAD DATA returned the error to the client application. Warning is used when LOAD DATA did not return the error to the client application and instead skipped or ignored the error. Note: If LOAD DATA ... ERRORS HANDLE returns a duplicate key error to the client application, the error is not logged to information_schema.LOAD_DATA_ERRORS.
ERROR_CODE The error code for the error.
ERROR_MESSAGE The message associated with the error. This value contains contextual information about the error that can be used for debugging purposes.
LOAD_DATA_LINE The erroneous line in the input file that LOAD DATA could not process. Load errors are typically caused by malformed data or attempting to write invalid schema types into a column, such as a NULL value into a non-nullable column.
LOAD_DATA_LINE_NUMBER The line number of the input file caused a parsing error while attempting to load data into the destination table. This line number can be correlated with the value of LOAD_DATA_LINE, which contains the invalid line’s text.
HOST The hostname or host IP address of the node that processed the erroneous line of the input file.
PORT The port number of the node that processed the erroneous line of the input file.
PARTITION Specifies the partition ID on the leaf node that processed the erroneous line of the input file. This value is NULL if the aggregator processed the line.

See the next section for example data that LOAD DATA ... ERRORS HANDLE populates in the information_schema.LOAD_DATA_ERRORS table.

Use the CLEAR LOAD ERRORS command to remove errors from information_schema.LOAD_DATA_ERRORS.

Error Handling

LOAD DATA has several options to handle errors that it encounters as it processes the input file. When you write a LOAD DATA statement, you can decide which option to use.

  • By default, LOAD DATA returns errors to the client application. Errors are returned one at a time.

  • To ignore duplicate key/index value errors in the input file, use the REPLACE clause to replace existing rows with input rows. This clause first deletes the existing rows that have the same value for a primary key or unique index as the input rows, and then inserts the new row.

  • To skip errors in the input file, use the SKIP ... ERRORS clause. Data in the erroneous lines will not be inserted into the destination table.

  • To ignore errors in the input file, use the IGNORE clause. This clause replaces invalid values with their defaults, discards extra fields, or discards erroneous lines completely.

Warning

In most cases, use SKIP ... ERRORS instead of IGNORE. If you use IGNORE without understanding how it behaves, LOAD DATA may produce unexpected results as it inserts data into the destination table.

MemSQL does not support the LOAD DATA ... [REPLACE | IGNORE | SKIP { ALL | CONSTRAINT | DUPLICATE KEY } ERRORS] semantics for ingesting data into columnstore tables with unique keys. As an alternative, you can write a Pipeline that calls a stored procedure, which handles duplicate key errors, see Example 7 in the CREATE PIPELINE topic.

The four error handling options are discussed in detail below.

Default Error Handling

By default, LOAD DATA returns errors to the client application. Errors are returned one at a time. If it returns an error, no data will be inserted into the destination table.

Example

Create a new table with a PRIMARY KEY column:

CREATE TABLE orders(
  id BIGINT PRIMARY KEY,
  customer_id INT,
  item_description VARCHAR(255),
  order_time TIMESTAMP NOT NULL
  );

The following CSV file will loaded be into this table as orders.csv. Note that line 2 has an extra column and there is a duplicate primary key value of 2 in line 4.

1,372,Apples,2016-05-09
3,307,Oranges,2016-07-31,1000
2,138,Pears,2016-07-14
2,236,Bananas,2016-06-23

Load the data into the table:

LOAD DATA INFILE 'orders.csv'
  INTO TABLE orders
  FIELDS TERMINATED BY ',';
****
ERROR 1262 (01000): Row 2 was truncated; it contained more data than there were input columns

After removing the extra column from row 2:

ERROR 1062 (23000): Leaf Error (127.0.0.1:3308): Duplicate entry '2' for key 'PRIMARY'

After removing the duplicate primary key entry, the LOAD DATA statement is successful and the input file is loaded into the table.

REPLACE Error Handling

MemSQL’s REPLACE behavior allows you to replace the existing rows with the new rows; only those rows that have the same value for a primary key or unique index as the input rows are replaced. In case of an error that arises due to duplicate key value, it first deletes the conflicting row in the destination that has the duplicate key value and then inserts the new row from the source file.

LOAD DATA inserts source file rows into the destination table in the order in which the rows appear in the source file. When REPLACE is specified, source files that contain duplicate unique or primary key values will be handled in the following way:

  • If the destination table’s schema specifies a unique or primary key column, and

  • The source file contains a row with the same primary or unique key value as the destination table, then

  • The row in the destination table that has the same unique or primary key value as the row in the source file will be deleted and a new row from the source file that matches the primary key value will be inserted into the destination table.

Note: If the source file contains multiple rows with the same primary or unique key value as the destination table, then only the last row in the source file with the same primary or unique key value (as the destination table) replaces the existing row in the destination table.

Note: REPLACE cannot be combined with SKIP DUPLICATE KEY ERRORS. The default behavior of MemSQL is to throw an error for duplicate keys. However, both REPLACE and SKIP DUPLICATE KEY ERRORS does not throw a duplicate key error; REPLACE replaces the old row with the new row, while SKIP DUPLICATE KEY ERRORS discards the new row and retains the old row.

Example

Create a new table with a PRIMARY KEY column:

CREATE TABLE orders(
  id BIGINT PRIMARY KEY,
  customer_id INT,
  item_description VARCHAR(255),
  order_time DATETIME NOT NULL
  );

A row with a primary key 4 is inserted as follows:

INSERT INTO orders VALUES(4,236,"Bananas",2016-06-23);

The following CSV file is loaded into the table as orders.csv. Note the duplicate primary key value of 4 in line 2:

1,372,Apples,2016-05-09
4,138,Pears,2016-07-14
3,307,Oranges,2016-07-31

Load the data into the table:

LOAD DATA INFILE 'orders.csv'
  REPLACE
  INTO TABLE orders
  FIELDS TERMINATED BY ','
  ERRORS HANDLE 'orders_errors';

Line 2 in the source file contained a duplicate primary key 4. The REPLACE error handler deletes the row 4,236,"Bananas",2016-06-23 in the destination table and replaces it with the value 4,138,Pears,2016-07-14 from the source file.

SKIP ... ERRORS Error Handling

MemSQL’s SKIP ... ERRORS behavior allows you to specify an error scenario that, when encountered, discards an offending row. Three kinds of error scenarios can be skipped:

  • SKIP DUPLICATE KEY ERRORS: Any row in the source data that contains a duplicate unique or primary key will be discarded. If the row contains invalid data other than a duplicate key, an error will be generated. See SKIP DUPLICATE KEY ERRORS below.
  • SKIP CONSTRAINT ERRORS: Inclusive of SKIP DUPLICATE KEY ERRORS. If a row violates a column’s NOT NULL constraint, or the row contains invalid JSON or Geospatial values, the row will be discarded. If the row contains invalid data outside the scope of constraint or invalid value errors, an error will be generated. See SKIP CONSTRAINT ERRORS below.
  • SKIP ALL ERRORS: Inclusive of SKIP DUPLICATE KEY ERRORS and SKIP CONSTRAINT ERRORS. Also includes any parsing errors in the row caused by issues such as an invalid number of fields. See SKIP ALL ERRORS below.

SKIP DUPLICATE KEY ERRORS

When SKIP DUPLICATE KEY ERRORS is specified, source files that contain duplicate unique or primary key values will be handled in the following way:

  • If the destination table’s schema specifies a unique or primary key column, and
  • The source file contains one or more rows with a duplicate key value that already exists in the destination table or exists elsewhere in the source file, then
  • Every duplicate row in the source file will be discarded and will not be inserted into the destination table.

SKIP DUPLICATE KEY ERRORS cannot be combined with REPLACE.

Example

Create a new table with a PRIMARY KEY column:

CREATE TABLE orders(
  id BIGINT PRIMARY KEY,
  customer_id INT,
  item_description VARCHAR(255),
  order_time TIMESTAMP NOT NULL
  );

The following CSV file will loaded be into this table as orders.csv. Note the duplicate primary key value of 2 in line 3:

1,372,Apples,2016-05-09
2,138,Pears,2016-07-14
2,236,Bananas,2016-06-23
3,307,Oranges,2016-07-31

Load the data into the table:

LOAD DATA INFILE 'orders.csv'
  SKIP DUPLICATE KEY ERRORS
  INTO TABLE orders
  FIELDS TERMINATED BY ','
  ERRORS HANDLE 'orders_errors';

Note that only 3 rows were inserted even though 4 rows were present in the source file. Line 3 in the source file contained a duplicate primary key, and you can verify that it was not inserted by querying the INFORMATION_SCHEMA.LOAD_DATA_ERRORS table:

SELECT load_data_line_number, load_data_line, error_message
  FROM INFORMATION_SCHEMA.LOAD_DATA_ERRORS
  WHERE handle = 'orders_errors'
  ORDER BY load_data_line_number;
****
+-----------------------+---------------------------+--------------------------------+
| load_data_line_number | load_data_line            | error_message                  |
+-----------------------+---------------------------+--------------------------------+
|                     3 | 2,236,Bananas,2016-06-23  | Duplicate entry for unique key |
+-----------------------+---------------------------+--------------------------------+

SKIP CONSTRAINT ERRORS

SKIP CONSTRAINT ERRORS is inclusive of SKIP DUPLICATE KEY ERRORS if REPLACE or ON DUPLICATE KEY UPDATE are not specified. It also applies to rows that violate a column’s NOT NULL constraint and fields that contain invalid JSON or Geospatial values, and handles the offending rows in the following ways:

NOT NULL Constraint

  • If a column in the destination table specifies a NOT NULL constraint, and
  • The source file contains one or more rows with a null value for the constraint column, then
  • The offending row(s) will be discarded and will not be inserted into the destination table.

Invalid JSON or Geospatial Data

  • If a column in the destination table specifies a JSON, GEOGRAPHYPOINT, or GEOGRAPHY data type, and
  • The source file contains one or more rows with invalid values for fields of these types, then
  • The offending row(s) will be discarded and will not be inserted into the destination table.

SKIP CONSTRAINT ERRORS can also be combined with REPLACE or ON DUPLICATE KEY UPDATE clauses.

Example

Create a new table with a JSON column type that also has a NOT NULL constraint:

CREATE TABLE orders(
  id BIGINT PRIMARY KEY,
  customer_id INT,
  item_description VARCHAR(255),
  order_properties JSON NOT NULL
  );

The following CSV file will loaded be into this table as orders.csv. Note the malformed JSON in line 2, as well as a null value (\N) for JSON in line 4:

1,372,Apples,{"order-date":"2016-05-09"}
2,138,Pears,{"order-date"}
3,236,Bananas,{"order-date":"2016-06-23"}
4,307,Oranges,\N

Load the data into the table:

LOAD DATA INFILE 'orders.csv'
  SKIP CONSTRAINT ERRORS
  INTO TABLE orders
  FIELDS TERMINATED BY ','
  ERRORS HANDLE 'orders_errors';

Note that only 2 rows were inserted even though 4 rows were present in the source file. Line 2 contained malformed JSON, and Line 4 contained an invalid null value. You can verify that both of these offending rows were not inserted by querying the INFORMATION_SCHEMA.LOAD_DATA_ERRORS table:

SELECT load_data_line_number, load_data_line, error_message
  FROM INFORMATION_SCHEMA.LOAD_DATA_ERRORS
  WHERE handle = 'orders_errors'
  ORDER BY load_data_line_number;
****
+-----------------------+-----------------------------+--------------------------------------------------------------+
| load_data_line_number | load_data_line              | error_message                                                |
+-----------------------+-----------------------------+--------------------------------------------------------------+
|                     2 | 2,138,Pears,{"order-date"}  | Invalid JSON value for column 'order_properties'             |
|                     4 | 4,307,Oranges,\N            | NULL supplied to NOT NULL column 'order_properties' at row 4 |
+-----------------------+-----------------------------+--------------------------------------------------------------+

SKIP ALL ERRORS

SKIP ALL ERRORS is inclusive of SKIP DUPLICATE KEY ERRORS and SKIP CONSTRAINT ERRORS in addition to any parsing error. Offending rows are handled in the following way:

  • If one or more rows in the source file cause ... DUPLICATE KEY ... or ... CONSTRAINT ... errors, or
  • If one or more rows in the source file cause parsing errors such as invalid delimiters or an invalid number of fields,
  • The offending row(s) will be discarded and will not be inserted into the destination table.

SKIP ALL ERRORS can also be combined with REPLACE or ON DUPLICATE KEY UPDATE.

Example

Create a new table with a JSON column type that also has a NOT NULL constraint:

CREATE TABLE orders(
  id BIGINT PRIMARY KEY,
  customer_id INT,
  item_description VARCHAR(255),
  order_properties JSON NOT NULL
  );

The following CSV file will loaded be into this table as orders.csv. There are three things wrong with this file:

  • Line 2 contains only 3 fields
  • Line 3 has a duplicate primary key
  • Line 4 has a null value for a NOT NULL constraint
1,372,Apples,{"order-date":"2016-05-09"}
2,138,Pears
1,236,Bananas,{"order-date":"2016-06-23"}
4,307,Oranges,\N

Load the data into the table:

LOAD DATA INFILE 'orders.csv'
  SKIP ALL ERRORS
  INTO TABLE orders
  FIELDS TERMINATED BY ','
  ERRORS HANDLE 'orders_errors';

Only 1 row was written, despite the source file containing 4 rows. Line 2 was dropped because it contained an invalid number of fields, Line 3 was dropped because it contained a duplicate primary key, and line 4 was dropped because it contained a null value for a NOT NULL constraint. You can verify that these offending rows were not inserted by querying the INFORMATION_SCHEMA.LOAD_DATA_ERRORS table:

SELECT load_data_line_number, load_data_line, error_message
  FROM INFORMATION_SCHEMA.LOAD_DATA_ERRORS
  WHERE handle = 'orders_errors'
  ORDER BY load_data_line_number;
****
+-----------------------+--------------------------------------------+--------------------------------------------------------------+
| load_data_line_number | load_data_line                             | error_message                                                |
+-----------------------+--------------------------------------------+--------------------------------------------------------------+
|                     2 | 2,138,Pears                                | Row 2 doesn't contain data for all columns                   |
|                     3 | 1,236,Bananas,{"order-date":"2016-06-23"}. | Duplicate entry for unique key                               |
|                     4 | 4,307,Oranges,\N                           | NULL supplied to NOT NULL column 'order_properties' at row 4 |
+-----------------------+--------------------------------------------+--------------------------------------------------------------+

IGNORE Error Handling

MemSQL’s IGNORE behavior is identical to MySQL’s IGNORE behavior, and exists only to support backwards compatibility with applications written for MySQL. IGNORE either discards malformed rows, discards extra fields, or replaces invalid values with default data type values. In addition, if an inserted row would have produced an error if IGNORE was not specified, it will be converted to a warning instead.

Consequences of Using IGNORE Instead of SKIP ERRORS

Unlike SKIP ... ERRORS which discards offending rows, IGNORE may change the inserted row’s data to ensure that it adheres to the table schema. This behavior can have serious repercussions for the data integrity of the destination table.

In a best case scenario where a malformed row uses the proper delimiters and contains the correct number of fields, the row can be partially salvaged. Any invalid values are updated with default values, and the modified row is written to the destination table. The result is that at least some of the source data was written to the destination table.

However, the worst case scenario can be severe. For example, if a row’s values are separated by an invalid delimiter, each field is updated with meaningless default values and the modified row is written to the destination table. For the sake of the table’s data integrity, it would have been better if the offending row was discarded. But a row with meaningless data was inserted instead.

Due to the potential consequences of using IGNORE, in most cases SKIP ... ERRORS is a better option. To understand IGNORE's behavior for each error scenario, continue reading the sections below:

Duplicate Unique or Primary Key Values

When IGNORE is specified, source files that contain duplicate unique or primary key values will be handled in the following way:

  • If the destination table’s schema specifies a unique or primary key column, and
  • The source file contains one or more rows with a duplicate key value that already exists in the destination table or exists elsewhere in the source file, then
  • Every duplicate row in the source file will be discarded (ignored) and will not be inserted into the destination table.
Example

Create a new table with a PRIMARY KEY column:

CREATE TABLE orders(
  id BIGINT PRIMARY KEY,
  customer_id INT,
  item_description VARCHAR(255),
  order_time DATETIME NOT NULL
  );

The following CSV file will loaded be into this table as orders.csv. Note the duplicate primary key value of 2 in line 3:

1,372,Apples,2016-05-09
2,138,Pears,2016-07-14
2,236,Bananas,2016-06-23
3,307,Oranges,2016-07-31

Load the data into the table:

LOAD DATA INFILE 'orders.csv'
  IGNORE
  INTO TABLE orders
  FIELDS TERMINATED BY ','
  ERRORS HANDLE 'orders_errors';

Note that only 3 rows were inserted even though 4 rows were present in the source file. Line 3 in the source file contained a duplicate primary key. You can verify that these offending rows were not inserted by querying the INFORMATION_SCHEMA.LOAD_DATA_ERRORS table:

SELECT load_data_line_number, load_data_line, error_message
  FROM INFORMATION_SCHEMA.LOAD_DATA_ERRORS
  WHERE handle = 'orders_errors'
  ORDER BY load_data_line_number;
****
+-----------------------+---------------------------+--------------------------------+
| load_data_line_number | load_data_line            | error_message                  |
+-----------------------+---------------------------+--------------------------------+
|                     3 | 2,236,Bananas,2016-06-23  | Duplicate entry for unique key |
+-----------------------+---------------------------+--------------------------------+

Line 3 in the source file contained a duplicate primary key and was discarded because line 2 was inserted first.

Values with Invalid Types According to the Destination Table’s Schema

When IGNORE is specified, source files that contain rows with invalid types that violate the destination table’s schema will be handled in the following way:

  • If the source file contains one or more rows with values that do not adhere to the destination table’s schema,
  • Each value of an invalid type in a row will be replaced with the default value of the appropriate type, and
  • The modified row(s) will be inserted into the destination table.

IGNORE behaves in a potentially unexpected way for columns that have a DEFAULT value specified. When an invalid value in the inserted row is replaced with the default value of the column’s type, the column’s DEFAULT value is ignored. Instead, the default value for the column’s data type is used.

Example

Create a new table with a PRIMARY KEY column:

CREATE TABLE orders(
  id BIGINT PRIMARY KEY,
  customer_id INT,
  item_description VARCHAR(255),
  order_time DATETIME NOT NULL
  );

The following CSV file will be loaded be into this table as orders.csv. Line 4 contains a NULL value for order_time, whereas the table schema does not allow NULL values for this field.

1,372,Apples,2016-05-09
2,138,Pears,2016-07-14
3,236,Bananas,2016-06-23
4,307,Oranges,\N

Load the data into the table:

LOAD DATA INFILE 'orders.csv'
  IGNORE
  INTO TABLE orders
  FIELDS TERMINATED BY ','
  ERRORS HANDLE 'orders_errors';

Note that 4 rows were inserted despite the fact that line 4 in the source file contained a null value for a NOT NULL column. You can verify the error with the fourth row by querying the INFORMATION_SCHEMA.LOAD_DATA_ERRORS table:

SELECT load_data_line_number, load_data_line, error_message
  FROM INFORMATION_SCHEMA.LOAD_DATA_ERRORS
  WHERE handle = 'orders_errors'
  ORDER BY load_data_line_number;
****
+-----------------------+------------------+--------------------------------------------------------+
| load_data_line_number | load_data_line   | error_message                                          |
+-----------------------+------------------+--------------------------------------------------------+
|                     4 | 4,307,Oranges,\N | NULL supplied to NOT NULL column 'order_time' at row 4 |
+-----------------------+------------------+--------------------------------------------------------+

To see what was inserted by replacing the invalid DATETIME value with a default value, query the table:

SELECT * FROM orders ORDER BY 1;
****
+----+-------------+------------------+---------------------+
| id | customer_id | item_description | order_time          |
+----+-------------+------------------+---------------------+
|  1 |         372 | Apples           | 2016-05-09 00:00:00 |
|  2 |         138 | Pears            | 2016-07-14 00:00:00 |
|  3 |         236 | Bananas          | 2016-06-23 00:00:00 |
|  4 |         307 | Oranges          | 0000-00-00 00:00:00 |
+----+-------------+------------------+---------------------+

In this example, the invalid null DATETIME value was replaced with its default value: 0000-00-00 00:00:00.

Rows That Contain an Invalid Number of Fields

When IGNORE is specified, source files that contain rows with an invalid number of fields will be handled in one of two ways:

Too Few Fields

  • If the source file contains one or more rows with too few fields according to the destination table’s schema,
  • Each row’s empty field(s) will be updated with default values, and
  • The row will be inserted into the destination table.

Too Many Fields

  • If the source file contains one or more rows with too many fields according to the destination table’s schema,
  • Each extra field in the row(s) will be discarded (ignored), and
  • The row will be inserted into the destination table.
Example

Create a new table with a PRIMARY KEY column:

CREATE TABLE orders(
  id BIGINT PRIMARY KEY,
  customer_id INT,
  item_description VARCHAR(255),
  order_time DATETIME NOT NULL
  );

The following CSV file will loaded be into this table as orders.csv. There are two things wrong with this file:

  • Line 2 contains only 3 fields instead of 4 and does not have a TIMESTAMP:
  • Line 4 contains an extra field, for a total of 5
1,372,Apples,2016-05-09
2,138,Pears
3,236,Bananas,2016-06-23
4,307,Oranges,2016-07-31,Berries

Load the data into the table:

LOAD DATA INFILE 'orders.csv'
  IGNORE
  INTO TABLE orders
  FIELDS TERMINATED BY ','
  ERRORS HANDLE 'orders_errors';

Note that 4 rows were inserted despite the invalid number of fields for two of the rows. You can verify the error with the fourth row by querying the INFORMATION_SCHEMA.LOAD_DATA_ERRORS table:

SELECT load_data_line_number, load_data_line, error_message
  FROM INFORMATION_SCHEMA.LOAD_DATA_ERRORS
  WHERE handle = 'orders_errors'
  ORDER BY load_data_line_number;
****
+-----------------------+----------------------------------+---------------------------------------------------------------------------+
| load_data_line_number | load_data_line                   | error_message                                                             |
+-----------------------+----------------------------------+---------------------------------------------------------------------------+
|                     2 | 2,138,Pears                      | Row 2 doesn't contain data for all columns                                |
|                     4 | 4,307,Oranges,2016-07-31,Berries | Row 4 was truncated; it contained more data than there were input columns |
+-----------------------+----------------------------------+---------------------------------------------------------------------------+

Note that there is a warning for the missing value in row 2 and the extra value in row 4. To see how the data was inserted, query the table:

SELECT * FROM orders ORDER BY 1;
****
+----+-------------+------------------+---------------------+
| id | customer_id | item_description | order_time          |
+----+-------------+------------------+---------------------+
|  1 |         372 | Apples           | 2016-05-09 00:00:00 |
|  2 |         138 | Pears            | 0000-00-00 00:00:00 |
|  3 |         236 | Bananas          | 2016-06-23 00:00:00 |
|  4 |         307 | Oranges          | 2016-07-31 00:00:00 |
+----+-------------+------------------+---------------------+

Line 2 did not have a DATETIME value, so the default value for its type was inserted instead. Line 4’s extra value was discarded, and otherwise the row was inserted with the expected data.

Performance Considerations

Shard keys Loading data into a table with a shard key requires reading the necessary columns on the aggregator to compute the shard key before sending data to the leaves. For CSV LOAD DATA only, it is recommended that columns included in the shard key appear earlier in input rows, if either the shard key design or the input format is flexible. Order does not significantly affect the performance of Avro or JSON LOAD DATA.

Retrieve loading status

The information_schema.LMV_LOAD_DATA_STATUS table reports information about rows and bytes read by in-progress LOAD DATA queries.

It also reports activity and database names, which you can use to find corresponding rows in workload profiling tables. See Management View Reference for more details.

Info

Result sets will only be returned if LMV_LOAD_DATA_STATUS is queried on the same aggregator as the in-progress LOAD_DATA queries.

information_schema.LMV_LOAD_DATA_STATUS Table Schema

Column Name Description
ID The connection ID.
ACTIVITY_NAME The name of the database activity.
DATABASE_NAME The name of the database associated with the file being loaded into the cluster.
BYTES_READ Bytes read from the input file stream.
ROWS_READ A count of rows read in from the source file (including skipped rows).
SELECT * FROM information_schema.LMV_LOAD_DATA_STATUS;
****
+------+------------------------------------+---------------+------------+-----------+
| ID   | ACTIVITY_NAME                      | DATABASE_NAME | BYTES_READ | ROWS_READ |
+------+------------------------------------+---------------+------------+-----------+
| 2351 | load_data_company_0e8dec6d07d9cba5 | trades        |   94380647 |    700512 |
+------+------------------------------------+---------------+------------+-----------+

Related Topics