Loading Data

COPY FROM

COPY <table> FROM '<file pattern>' [WITH (<property> = value, ...)];

<file pattern> must be local on the server. The file pattern can contain wildcards if you want to load multiple files. In addition to CSV, TSV, and TXT files, you can import compressed files in TAR, ZIP,7-ZIP, RAR, GZIP, BZIP2, or TGZ format.

You can import client-side files (\copy command in mapdql) but it is significantly slower. For large files, MapD recommends that you first scp the file to the server, and then issue the COPY command.

<property> in the optional WITH clause can be:

  • delimiter: a single-character string for the delimiter between input fields. The default is ",", that is, as a CSV file.
  • nulls: a string pattern indicating a field is NULL. By default, an empty string or \N means NULL.
  • header: can be either 'true' or 'false' indicating whether the input file has a header line in Line 1 that should be skipped. The default is 'true'.
  • escape: a single-character string for escaping quotes. The default is the quote character itself.
  • quoted: 'true' or 'false' indicating whether the input file contains quoted fields. The default is 'true'.
  • quote: a single-character string for quoting a field. The default quote character is double quote ". All characters inside quotes are imported “as is,” except for line delimiters.
  • line_delimiter a single-character string for terminating each line. The default is "\n".
  • array: a two-character string consisting of the start and end characters surrounding an array. The default is {}. For example, data to be inserted into a table with a string array in the second column (e.g. BOOLEAN, STRING[], INTEGER) can be written as true,{value1,value2,value3},3.
  • array_delimiter: a single-character string for the delimiter between input values contained within an array. The default is ",".
  • threads number of threads for performing the data import. The default is the number of CPU cores on the system.
  • max_reject number of records that the COPY statement allows to be rejected before terminating the COPY command. Records can be rejected for a number of reasons: for example, invalid content in a field, or an incorrect number of columns. The details of the rejected records are reported in the ERROR log. COPY returns a message identifying how many records are rejected. The records that are not rejected are inserted into the table, even if the COPY stops due to the max_reject count being reached. The default is 100,000.
  • plain_text: This parameter indicates that the input file is a plain text file so as to bypass the libarchive decompression utility. CSV, TSV, and TXT are always handled as plain text by default.

Note: by default, the CSV parser assumes one row per line. To import a file with multiple lines in a single field, specify threads = 1 in the WITH clause.

Examples:

COPY tweets from '/tmp/tweets.csv' WITH (nulls = 'NA');
COPY tweets from '/tmp/tweets.tsv' WITH (delimiter = '\t', quoted = 'false');
COPY tweets from '/tmp/*'          WITH (header='false');

SQLImporter

java -cp [MapD JDBC driver]:[3rd party JDBC driver]
com.mapd.utility.SQLImporter -t [MapD table name] -su [external source user]
-sp [external source password] -c "jdbc:[external
source]://server:port;DatabaseName=some_database" -ss "[select statement]"

usage: SQLImporter
-b,--bufferSize <arg>      Transfer buffer size
-c,--jdbcConnect <arg>     JDBC Connection string
-d,--driver <arg>          JDBC driver class
-db,--database <arg>       MapD Database
-f,--fragmentSize <arg>    Table fragment size
-i <arg>                   Path to initialization file.
-p,--passwd <arg>          MapD Password
--port <arg>               MapD Port
-r <arg>                   Row Load Limit
-s,--server <arg>          MapD Server
-sp,--sourcePasswd <arg>   Source Password
-ss,--sqlStmt <arg>        SQL Select statement
-su,--sourceUser <arg>     Source User
-t,--targetTable <arg>     MapD Target Table
-tr,--truncate             Drop and recreate the table, if it exists
-u,--user <arg>            MapD User

SQL Importer executes a select statement on another database via JDBC and brings the result set into MapD Core.

If the table does not, SQL Importer creates the table in MapD Core.

If the truncate flag is set, it truncates the contents of the file.

If the file exists and truncate is not set, data import fails if the table does not match the SELECT statement metadata.

MapD recommends that you use a service account with read-only permissions when accessing data from a remote database.

The -i argument provides a path to an initialization file. Each line of the file is sent as a SQL statement to the remote server from which the data is copied. This can be used to set additional custom parameters before the data is loaded.

MySQL Example:

java -cp mapd-1.0-SNAPSHOT-jar-with-dependencies.jar:
mysql/mysql-connector-java-5.1.38/mysql-connector-java-5.1.38-bin.jar
com.mapd.utility.SQLImporter -t test1 -sp mypassword -su myuser
-c jdbc:mysql://localhost -ss "select * from employees.employees"

SQLServer Example:

java -cp
/path/to/mapd/bin/mapd-1.0-SNAPSHOT-jar-with-dependencies.jar:/path/to/sqljdbc4.jar
com.mapd.utility.SQLImporter -d com.microsoft.sqlserver.jdbc.SQLServerDriver -t
mapd_target_table -su source_user -sp source_pwd -c
"jdbc:sqlserver://server:port;DatabaseName=some_database" -ss "select top 10 *
from dbo.some_table"

PostgreSQL Example:

java -cp
/p/to/mapd/bin/mapd-1.0-SNAPSHOT-jar-with-dependencies.jar:
/p/to/postgresql-9.4.1208.jre6.jar
com.mapd.utility.SQLImporter -t mapd_target_table -su source_user -sp
source_pwd -c "jdbc:postgresql://server/database" -ss "select * from some_table
where transaction_date > '2014-01-01'"

StreamInsert

Stream data into MapD Core by attaching the StreamInsert program to the end of a data stream. The data stream can be another program printing to standard out, a Kafka endpoint, or any other real-time stream output. You can specify the appropriate batch size, according to the expected stream rates and your insert frequency. The target table must exist before you attempt to stream data into the table.

<data stream> | StreamInsert <table name> <database name> \
{-u|--user} <user> {-p|--passwd} <password> [{--host} <hostname>] \
[--port <port number>][--delim <delimiter>][--null <null string>] \
[--line <line delimiter>][--batch <batch size>][{-t|--transform} \
transformation ...][--retry_count <num_of_retries>] \
[--retry_wait <wait in secs>][--print_error][--print_transform]
Setting Default Description
<table_name> n/a Name of the target table in MapD
<database_name> n/a Name of the target database in MapD
-u n/a User name
-p n/a User password
--host n/a Name of MapD host
--delim comma (,) Field delimiter, in single quotes
--line newline (\n) Line delimiter, in single quotes
--batch 10000 Number of records in a batch
--retry_count 10 Number of attempts before job fails
--retry_wait 5 Wait time in seconds after server connection failure
--null n/a String that represents null values
--port 9091 Port number for MapD Core on localhost
-t|--transform n/a Regex transformation
--print_error False Print error messages
--print_transform False Print description of transform.
--help n/a List options

For more information on creating regex transformation statements, see RegEx Replace.

Example:

cat file.tsv | /path/to/mapd/SampleCode/StreamInsert stream_example \
mapd --host localhost --port 9091 -u imauser -p imapassword \
--delim '\t' --batch 1000

Importing AWS S3 Files

You can use the SQL COPY FROM statement to import files stored on AWS S3 into a MapD table, in much the same way you would with local files. In the WITH clause, specify the S3 credentials and region information of the bucket accessed.

COPY <table> FROM '<S3_file_URL>' WITH (s3_access_key = <key_name>,s3_secret_key = <key_name>,s3_region = <region>);

Access key, secret key, and region are required. For information about AWS S3 credentials, see https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html#access-keys-and-secret-access-keys.

The following examples show failed and successful attempts to copy the table trips from AWS S3.

mapdql> COPY trips FROM 's3://mapd-s3-no-access/trip_data_9.gz';
Exception: failed to list objects of s3 url 's3://mapd-s3-no-access/trip_data_9.gz': AccessDenied: Access Denied

mapdql> COPY trips FROM 's3://mapd-s3-no-access/trip_data_9.gz' with (s3_access_key='xxxxxxxxxx',s3_secret_key='yyyyyyyyy');
Exception: failed to list objects of s3 url 's3://mapd-s3-no-access/trip_data_9.gz': AuthorizationHeaderMalformed: Unable to parse ExceptionName: AuthorizationHeaderMalformed Message: The authorization header is malformed; the region 'us-east-1' is wrong; expecting 'us-west-1'

mapdql> COPY trips FROM 's3://mapd-parquet-testdata/trip.compressed/trip_data_9.csv' with (s3_access_key=’xxxxxxxx’,s3_secret_key='yyyyyyyy',s3_region='us-west-1');
Result
Loaded: 100 recs, Rejected: 0 recs in 0.361000 secs

The following example imports all the files in the trip.compressed directory.

mapdql> copy trips from 's3://mapd-parquet-testdata/trip.compressed/' with (s3_access_key=’xxxxxxxx’,s3_secret_key='yyyyyyyy',s3_region='us-west-1');
Result
Loaded: 105200 recs, Rejected: 0 recs in 1.890000 secs

trips Table

The table trips is created with the following statement:

mapdql> \d trips
        CREATE TABLE trips (
        medallion TEXT ENCODING DICT(32),
        hack_license TEXT ENCODING DICT(32),
        vendor_id TEXT ENCODING DICT(32),
        rate_code_id SMALLINT,
        store_and_fwd_flag TEXT ENCODING DICT(32),
        pickup_datetime TIMESTAMP,
        dropoff_datetime TIMESTAMP,
        passenger_count SMALLINT,
        trip_time_in_secs INTEGER,
        trip_distance DECIMAL(14,2),
        pickup_longitude DECIMAL(14,2),
        pickup_latitude DECIMAL(14,2),
        dropoff_longitude DECIMAL(14,2),
        dropoff_latitude DECIMAL(14,2))
WITH (FRAGMENT_SIZE = 75000000);

HDFS

Consume a CSV or Parquet file residing in HDFS into MapD Core.

Copy the MapD JDBC driver into the sqoop lib, normally /usr/lib/sqoop/lib/

Example:

sqoop-export --table alltypes --export-dir /user/cloudera/ \
--connect "jdbc:mapd:192.168.122.1:9091:mapd" \
--driver com.mapd.jdbc.MapDDriver --username imauser \
--password imapassword --direct --batch

Troubleshooting: How to Avoid Duplicate Rows

To detect duplication prior to loading data into MapD Core Database, you can perform the following steps. For this example, the files are labeled A,B,C...Z.

  1. Load file A into table MYTABLE.

  2. Run the following query.

    select t1.uniqueCol from MYTABLE t1 join MYTABLE t2 on t1.uCol = t2.uCol;

    There should be no rows returned; if rows are returned, your first A file is not unique.

  3. Load file B into table TEMPTABLE.

  4. Run the following query.

    select t1.uniqueCol from MYTABLE t1 join TEMPTABLE t2 on t1.uCol = t2.uCol;

    There should be no rows returned if file B is unique. Fix B if the information is not unique using details from the selection.

  5. Load the fixed B file into MYFILE.

  6. Drop table TEMPTABLE.

  7. Repeat steps 3-6 for the rest of the set for each file prior to loading the data to the real MYTABLE instance.

KafkaImporter

MapD can be used as a data bus. You can ingest data from an existing Kafka producer to an existing table in MapD using KafkaImporter on the command line.

NOTE: KafkaImporter requires a functioning Kafka cluster. See the Kafka website and the Confluent schema registry documentation.

KafkaImporter <table_name> <database_name> {-u|--user <user_name> \
{-p|--passwd <user_password>} [{--host} <hostname>] \
[--port <mapd_core_port>] [--delim <delimiter>] [--batch <batch_size>] \
[{-t|--transform} transformation ...] [retry_count <number_of_retries>] \
[--retry_wait <delay_in_seconds>] --null <null_value_string> \
[--line <line delimiter>] --brokers=<broker_name:broker_port> --group-id=
<kafka_group_id> --topic=<topic_type> [--print_error][--print_transform]
Setting Default Description
<table_name> n/a Name of the target table in MapD
<database_name> n/a Name of the target database in MapD
-u n/a User name
-p n/a User password
--host n/a Name of MapD host
--delim comma (,) Field delimiter, in single quotes
--line newline (\n) Line delimiter, in single quotes
--batch 10000 Number of records in a batch
--retry_count 10 Number of attempts before job fails
--retry_wait 5 Wait time in seconds after server connection failure
--null n/a String that represents null values
--port 9091 Port number for MapD Core on localhost
-t|--transform n/a Regex transformation
--print_error False Print error messages
--print_transform False Print description of transform
--help n/a List options
--brokers localhost:9092 One or more brokers
--group-id n/a Kafka group ID
--topic n/a The Kafka topic to be ingested

Configure KafkaImporter to use your target table. KafkaImporter listens to a pre-defined Kafka topic associated with your table. You must create the table before using the KafkaImporter utility. For example, you might have a table named customer_site_visit_events that listens to a topic named customer_site_visit_events_topic.

The data format must be a record-level format supported by MapD.

KafkaImporter listens to the topic, validates records against the target schema, and ingests topic batches of your designated size to the target table. Rejected records use the existing reject reporting mechanism. You can start, shutdown, and configure KafkaImporter independent of the MapD core engine. If KafkaImporter is running but the database shuts down, KafkaImporter shuts down as well. Reads from the topic are non-destructive.

KafkaImporter is not responsible for event ordering - a first class streaming platform outside MapD (for example, Spark streaming, flink) should handle the stream processing. MapD ingests the end-state stream of post-processed events.

KafkaImporter does not handle dynamic schema creation on first ingest, but must be configured with a specific target table (and its schema) as the basis. There is a 1:1 correspondence between target table and topic.

./KafkaImporter tweets_small mapd
-u imauser
-p imapassword
--delim '\t'
--batch 100000
--retry_count 360
--retry_wait 10
--null null
--port 9999
--brokers=localhost:9092
--group-id=testImport1
--topic=tweet

StreamImporter

MapD can be used as a data bus. You can ingest data from a data stream to an existing table in MapD using StreamImporter on the command line.

StreamImporter <table_name> <database_name> {-u|--user <user_name> \
{-p|--passwd <user_password>} [{--host} <hostname>] \
[--port <mapd_core_port>] [--delim <delimiter>] [--batch <batch_size>] \
[{-t|--transform} transformation ...] [retry_count <number_of_retries>] \
[--retry_wait <delay_in_seconds>] --null <null_value_string> \
[--line <line delimiter>] [--print_error][--print_transform]
Setting Default Description
<table_name> n/a Name of the target table in MapD
<database_name> n/a Name of the target database in MapD
-u n/a User name
-p n/a User password
--host n/a Name of MapD host
--delim comma (,) Field delimiter, in single quotes
--line newline (\n) Line delimiter, in single quotes
--batch 10000 Number of records in a batch
--retry_count 10 Number of attempts before job fails
--retry_wait 5 Wait time in seconds after server connection failure
--null n/a String that represents null values
--port 9091 Port number for MapD Core on localhost
-t|--transform n/a Regex transformation
--print_error False Print error messages
--print_transform False Print description of transform.
--help n/a List options

Configure StreamImporter to use your target table. StreamImporter listens to a pre-defined data stream associated with your table. You must create the table before using the StreamImporter utility.

The data format must be a record-level format supported by MapD.

StreamImporter listens to the stream, validates records against the target schema, and ingests batches of your designated size to the target table. Rejected records use the existing reject reporting mechanism. You can start, shut down, and configure StreamImporter independent of the MapD core engine. If StreamImporter is running but the database shuts down, StreamImporter shuts down as well. Reads from the stream are non-destructive.

StreamImporter is not responsible for event ordering - a first class streaming platform outside MapD (for example, Spark streaming, flink) should handle the stream processing. MapD ingests the end-state stream of post-processed events.

StreamImporter does not handle dynamic schema creation on first ingest, but must be configured with a specific target table (and its schema) as the basis.

There is a 1:1 correspondence between target table and a stream record.

./StreamImporter tweets_small mapd
-u imauser
-p imapassword
--delim '\t'
--batch 100000
--retry_count 360
--retry_wait 10
--null null
--port 9999