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The Java Database Connection (JDBC) importer allows to fetch data from JDBC sources for indexing into Elasticsearch.
The JDBC importer was designed for tabular data. If you have tables with many joins, the JDBC importer is limited in the way to reconstruct deeply nested objects to JSON and process object semantics like object identity. Though it would be possible to extend the JDBC importer with a mapping feature where all the object properties could be specified, the current solution is focused on rather simple tabular data streams.
Assuming you have a table of name orders with a primary key in column id,
you can issue this from the command line
bin=$JDBC_IMPORTER_HOME/bin
lib=$JDBC_IMPORTER_HOME/lib
echo '{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"user" : "",
"password" : "",
"sql" : "select *, id as _id from orders"
}
}' | java \
-cp "${lib}/*" \
-Dlog4j.configurationFile=${bin}/log4j2.xml \
org.xbib.tools.Runner \
org.xbib.tools.JDBCImporter
And that's it. Now you can check your Elasticsearch cluster for the index jdbc or your Elasticsearch logs
about what happened.
| Release date | JDBC Importer version | Elasticsearch version |
|---|---|---|
| Aug 28 2016 | 2.3.4.1 | 2.3.4 |
| Aug 1 2016 | 2.3.4.0 | 2.3.4 |
| Jul 6 2016 | 2.3.3.1 | 2.3.3 |
| May 28 2016 | 2.3.3.0 | 2.3.3 |
| May 27 2016 | 2.3.2.0 | 2.3.2 |
| Apr 9 2016 | 2.3.1.0 | 2.3.1 |
| Apr 9 2016 | 2.2.1.0 | 2.2.1 |
| Feb 5 2016 | 2.2.0.0 | 2.2.0 |
| Dec 23 2015 | 2.1.1.2 | 2.1.1 |
| Nov 29 2015 | 2.1.0.0 | 2.1.0 |
| Oct 29 2015 | 2.0.0.1 | 2.0.0 |
| Oct 28 2015 | 2.0.0.0 | 2.0.0 |
| Oct 23 2015 | 1.7.3.0 | 1.7.3 |
| Sep 29 2015 | 1.7.2.1 | 1.7.2 |
| Jul 24 2015 | 1.7.0.1 | 1.7.0 |
| Jul 24 2015 | 1.6.0.1 | 1.6.0 |
| Jun 2015 | 1.5.2.0 | 1.5.2 |
JDBC importer 2.3.4.0
http://xbib.org/repository/org/xbib/elasticsearch/importer/elasticsearch-jdbc/2.3.4.0/elasticsearch-jdbc-2.3.4.0-dist.zip
-
in the following steps replace
<version>by one of the versions above, e.g.1.7.0.0 -
download the JDBC importer distribution
wget http://xbib.org/repository/org/xbib/elasticsearch/importer/elasticsearch-jdbc/<version>/elasticsearch-jdbc-<version>-dist.zip -
unpack
unzip elasticsearch-jdbc-<version>-dist.zip -
go to the unpacked directory (we call it $JDBC_IMPORTER_HOME)
cd elasticsearch-jdbc-<version> -
if you do not find the JDBC driver jar in the
libdirectory, download it from your vendor's site and put the driver jar into thelibfolder -
modify script in the
bindirectory to your needs (Elasticsearch cluster address) -
run script with a command that starts
org.xbib.tools.JDBCImporterwith thelibdirectory on the classpath
The JDBC importer comes with open source JDBC drivers bundled for your convenience. They are not part of the JDBC importer, hence, there is no support and no guarantee the bundled drivers will work. Please read the JDBC driver license files attached in the distribution. JDBC importer does not link against the code of the drivers. If you do not want the drivers jars, they can be safely removed or replaced by other JDBC drivers at your choice.
The Maven project site is available at Github
All feedback is welcome! If you find issues, please post them at Github
You find this software useful and want to honor me for my work? Please donate. Donations will also help to keep up the development of open source Elasticsearch add-ons.
The relational data is internally transformed into structured JSON objects for the schema-less indexing model of Elasticsearch documents.
The importer can fetch data from RDBMS while multithreaded bulk mode ensures high throughput when indexing to Elasticsearch.
The general form of a JDBC import specification is a JSON object.
{
"type" : "jdbc",
"jdbc" : {
<definition>
}
}
Example:
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"user" : "",
"password" : "",
"sql" : "select * from orders",
"index" : "myindex",
"type" : "mytype",
...
}
}
The importer can either be executed via stdin (for example with echo)
bin=$JDBC_IMPORTER_HOME/bin
lib=$JDBC_IMPORTER_HOME/lib
echo '{
...
}' | java \
-cp "${lib}/*" \
-Dlog4j.configurationFile=${bin}/log4j2.xml \
org.xbib.tools.Runner \
org.xbib.tools.JDBCImporter
or with explicit file name parameter from command line. Here is an example
where statefile.json is a file which is loaded before execution.
java \
-cp "${lib}/*" \
-Dlog4j.configurationFile=${bin}/log4j2.xml \
org.xbib.tools.Runner \
org.xbib.tools.JDBCImporter \
statefile.json
This style is convenient for subsequent execution controlled by the statefile parameter
if statefile is set to statefile.json.
Here is the list of parameters for the jdbc block in the definition.
strategy - the strategy of the JDBC importer, currently implemented: "standard", "column"
url - the JDBC driver URL
user - the JDBC database user
password - the JDBC database password
sql - SQL statement(s), either a string or a list. If a statement ends with .sql, the statement is looked up in the file system. Example for a list of SQL statements:
"sql" : [
{
"statement" : "select ... from ... where a = ?, b = ?, c = ?",
"parameter" : [ "value for a", "value for b", "value for c" ]
},
{
"statement" : "insert into ... where a = ?, b = ?, c = ?",
"parameter" : [ "value for a", "value for b", "value for c" ],
"write" : "true"
},
{
"statement" : ...
}
]
sql.statement - the SQL statement
sql.write - boolean flag, if true, the SQL statement is interpreted as an insert/update statement that needs write access (default: false).
sql.callable - boolean flag, if true, the SQL statement is interpreted as a JDBC CallableStatement for stored procedures (default: false).
sql.parameter - bind parameters for the SQL statement (in order). Some special values can be used with the following meanings:
$now- the current timestamp$state- the state, one of: BEFORE_FETCH, FETCH, AFTER_FETCH, IDLE, EXCEPTION$metrics.counter- a counter$lastrowcount- number of rows from last statement$lastexceptiondate- SQL timestamp of last exception$lastexception- full stack trace of last exception$metrics.lastexecutionstart- SQL timestamp of the time when last execution started$metrics.lastexecutionend- SQL timestamp of the time when last execution ended$metrics.totalrows- total number of rows fetched$metrics.totalbytes- total number of bytes fetched$metrics.failed- total number of failed SQL executions$metrics.succeeded- total number of succeeded SQL executions
locale - the default locale (used for parsing numerical values, floating point character. Recommended values is "en_US")
timezone - the timezone for JDBC setTimestamp() calls when binding parameters with timestamp values
rounding - rounding mode for parsing numeric values. Possible values "ceiling", "down", "floor", "halfdown", "halfeven", "halfup", "unnecessary", "up"
scale - the precision of parsing numeric values
autocommit - true if each statement should be automatically executed. Default is false
fetchsize - the fetchsize for large result sets, most drivers use this to control the amount of rows in the buffer while iterating through the result set
max_rows - limit the number of rows fetches by a statement, the rest of the rows is ignored
max_retries - the number of retries to (re)connect to a database
max_retries_wait - a time value for the time that should be waited between retries. Default is "30s"
resultset_type - the JDBC result set type, can be TYPE_FORWARD_ONLY, TYPE_SCROLL_SENSITIVE, TYPE_SCROLL_INSENSITIVE. Default is TYPE_FORWARD_ONLY
resultset_concurrency - the JDBC result set concurrency, can be CONCUR_READ_ONLY, CONCUR_UPDATABLE. Default is CONCUR_UPDATABLE
ignore_null_values - if NULL values should be ignored when constructing JSON documents. Default is false
detect_geo - if geo polygons / points in SQL columns should be parsed when constructing JSON documents. Default is true
detect_json - if json structures in SQL columns should be parsed when constructing JSON documents. Default is true
prepare_database_metadata - if the driver metadata should be prepared as parameters. Default is false
prepare_resultset_metadata - if the result set metadata should be prepared as parameters. Default is false
column_name_map - a map of aliases that should be used as a replacement for column names of the database. Useful for Oracle 30 char column name limit. Default is null
query_timeout - a second value for how long an SQL statement is allowed to be executed before it is considered as lost. Default is 1800
connection_properties - a map for the connection properties for driver connection creation. Default is null
schedule - a single or a list of cron expressions for scheduled execution. Syntax is equivalent to the
Quartz cron expression format (see below for syntax)
threadpoolsize - a thread pool size for the scheduled executions for schedule parameter. If set to 1, all jobs will be executed serially. Default is 4.
interval - a time value for the delay between two runs (default: not set)
elasticsearch.cluster - Elasticsearch cluster name
elasticsearch.host - array of Elasticsearch host specifications (host name or host:port)
elasticsearch.port - port of Elasticsearch host
elasticsearch.autodiscover - if true, JDBC importer will try to connect to all cluster nodes. Default is false
max_bulk_actions - the length of each bulk index request submitted (default: 10000)
max_concurrent_bulk_requests - the maximum number of concurrent bulk requests (default: 2 * number of CPU cores)
max_bulk_volume - a byte size parameter for the maximum volume allowed for a bulk request (default: "10m")
max_request_wait - a time value for the maximum wait time for a response of a bulk request (default: "60s")
flush_interval - a time value for the interval period of flushing index docs to a bulk action (default: "5s")
index - the Elasticsearch index used for indexing
type - the Elasticsearch type of the index used for indexing
index_settings - optional settings for the Elasticsearch index
type_mapping - optional mapping for the Elasticsearch index type
statefile - name of a file where the JDBC importer reads or writes state information
metrics.lastexecutionstart - the UTC date/time of the begin of the last execution of a single fetch
metrics.lastexecutionend - the UTC date/time of the end of the last execution of a single fetch
metrics.counter - a counter for metrics, will be incremented after each single fetch
metrics.enabled - if true, metrics logging is enabled. Default is false
metrics.interval - the interval between metrics logging. Default is 30 seconds.
metrics.logger.plain - if true, write metrics log messages in plain text format. Default is false
metrics.logger.json - if true, write metric log messages in JSON format. Default is false
{
"jdbc" : {
"strategy" : "standard",
"url" : null,
"user" : null,
"password" : null,
"sql" : null,
"locale" : /* equivalent to Locale.getDefault().toLanguageTag() */,
"timezone" : /* equivalent to TimeZone.getDefault() */,
"rounding" : null,
"scale" : 2,
"autocommit" : false,
"fetchsize" : 10, /* if URL contains MySQL JDBC driver URL, this is Integer.MIN */
"max_rows" : 0,
"max_retries" : 3,
"max_retries_wait" : "30s",
"resultset_type" : "TYPE_FORWARD_ONLY",
"resultset_concurreny" : "CONCUR_UPDATABLE",
"ignore_null_values" : false,
"prepare_database_metadata" : false,
"prepare_resultset_metadata" : false,
"column_name_map" : null,
"query_timeout" : 1800,
"connection_properties" : null,
"schedule" : null,
"interval" : 0L,
"threadpoolsize" : 1,
"index" : "jdbc",
"type" : "jdbc",
"index_settings" : null,
"type_mapping" : null,
"max_bulk_actions" : 10000,
"max_concurrent_bulk_requests" : 2 * available CPU cores,
"max_bulk_volume" : "10m",
"max_request_wait" : "60s",
"flush_interval" : "5s"
}
}
Setting a cron expression in the parameter schedule enables repeated (or time scheduled) runs.
You can also define a list of cron expressions (in a JSON array) to schedule for many different time schedules.
Example of a schedule parameter:
"schedule" : "0 0-59 0-23 ? * *"
This executes JDBC importer every minute, every hour, all the days in the week/month/year.
The following documentation about the syntax of the cron expression is copied from the Quartz scheduler javadoc page.
Cron expressions provide the ability to specify complex time combinations such as "At 8:00am every Monday through Friday" or "At 1:30am every last Friday of the month".
Cron expressions are comprised of 6 required fields and one optional field separated by white space. The fields respectively are described as follows:
| Field Name | Allowed Values | Allowed Special Characters |
|---|---|---|
| Seconds | 0-59 | , - * / |
| Minutes | 0-59 | , - * / |
| Hours | 0-23 | , - * / |
| Day-of-month | 1-31 | , - * ? / L W |
| Month | 1-12 or JAN-DEC | , - * / |
| Day-of-Week | 1-7 or SUN-SAT | , - * ? / L # |
| Year (Optional) | empty, 1970-2199 | , - * / |
The '' character is used to specify all values. For example, "" in the minute field means "every minute".
The '?' character is allowed for the day-of-month and day-of-week fields. It is used to specify 'no specific value'. This is useful when you need to specify something in one of the two fields, but not the other.
The '-' character is used to specify ranges For example "10-12" in the hour field means "the hours 10, 11 and 12".
The ',' character is used to specify additional values. For example "MON,WED,FRI" in the day-of-week field means "the days Monday, Wednesday, and Friday".
The '/' character is used to specify increments. For example "0/15" in the seconds field means "the seconds 0, 15, 30, and 45". And "5/15" in the seconds field means "the seconds 5, 20, 35, and 50". Specifying '*' before the '/' is equivalent to specifying 0 is the value to start with. Essentially, for each field in the expression, there is a set of numbers that can be turned on or off. For seconds and minutes, the numbers range from 0 to 59. For hours 0 to 23, for days of the month 0 to 31, and for months 1 to 12. The "/" character simply helps you turn on every "nth" value in the given set. Thus "7/6" in the month field only turns on month "7", it does NOT mean every 6th month, please note that subtlety.
The 'L' character is allowed for the day-of-month and day-of-week fields. This character is short-hand for "last", but it has different meaning in each of the two fields. For example, the value "L" in the day-of-month field means "the last day of the month" - day 31 for January, day 28 for February on non-leap years. If used in the day-of-week field by itself, it simply means "7" or "SAT". But if used in the day-of-week field after another value, it means "the last xxx day of the month" - for example "6L" means "the last friday of the month". You can also specify an offset from the last day of the month, such as "L-3" which would mean the third-to-last day of the calendar month. When using the 'L' option, it is important not to specify lists, or ranges of values, as you'll get confusing/unexpected results.
The 'W' character is allowed for the day-of-month field. This character is used to specify the weekday (Monday-Friday) nearest the given day. As an example, if you were to specify "15W" as the value for the day-of-month field, the meaning is: "the nearest weekday to the 15th of the month". So if the 15th is a Saturday, the trigger will fire on Friday the 14th. If the 15th is a Sunday, the trigger will fire on Monday the 16th. If the 15th is a Tuesday, then it will fire on Tuesday the 15th. However if you specify "1W" as the value for day-of-month, and the 1st is a Saturday, the trigger will fire on Monday the 3rd, as it will not 'jump' over the boundary of a month's days. The 'W' character can only be specified when the day-of-month is a single day, not a range or list of days.
The 'L' and 'W' characters can also be combined for the day-of-month expression to yield 'LW', which translates to "last weekday of the month".
The '#' character is allowed for the day-of-week field. This character is used to specify "the nth" XXX day of the month. For example, the value of "6#3" in the day-of-week field means the third Friday of the month (day 6 = Friday and "#3" = the 3rd one in the month). Other examples: "2#1" = the first Monday of the month and "4#5" = the fifth Wednesday of the month. Note that if you specify "#5" and there is not 5 of the given day-of-week in the month, then no firing will occur that month. If the '#' character is used, there can only be one expression in the day-of-week field ("3#1,6#3" is not valid, since there are two expressions).
The legal characters and the names of months and days of the week are not case sensitive.
Note: Support for specifying both a day-of-week and a day-of-month value is not complete (you'll need to use the '?' character in one of these fields). Overflowing ranges is supported - that is, having a larger number on the left hand side than the right. You might do 22-2 to catch 10 o'clock at night until 2 o'clock in the morning, or you might have NOV-FEB. It is very important to note that overuse of overflowing ranges creates ranges that don't make sense and no effort has been made to determine which interpretation CronExpression chooses. An example would be "0 0 14-6 ? * FRI-MON".
One of the advantage of SQL queries is the join operation. From many tables, new tuples can be formed.
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"user" : "",
"password" : "",
"sql" : "select \"relations\" as \"_index\", orders.customer as \"_id\", orders.customer as \"contact.customer\", employees.name as \"contact.employee\" from orders left join employees on employees.department = orders.department order by _id"
}
}
For example, these rows from SQL
mysql> select "relations" as "_index", orders.customer as "_id", orders.customer as "contact.customer", employees.name as "contact.employee" from orders left join employees on employees.department = orders.department order by _id;
+-----------+-------+------------------+------------------+
| _index | _id | contact.customer | contact.employee |
+-----------+-------+------------------+------------------+
| relations | Big | Big | Smith |
| relations | Large | Large | Müller |
| relations | Large | Large | Meier |
| relations | Large | Large | Schulze |
| relations | Huge | Huge | Müller |
| relations | Huge | Huge | Meier |
| relations | Huge | Huge | Schulze |
| relations | Good | Good | Müller |
| relations | Good | Good | Meier |
| relations | Good | Good | Schulze |
| relations | Bad | Bad | Jones |
+-----------+-------+------------------+------------------+
11 rows in set (0.00 sec)
will generate fewer JSON objects for the index relations.
index=relations id=Big {"contact":{"employee":"Smith","customer":"Big"}}
index=relations id=Large {"contact":{"employee":["Müller","Meier","Schulze"],"customer":"Large"}}
index=relations id=Huge {"contact":{"employee":["Müller","Meier","Schulze"],"customer":"Huge"}}
index=relations id=Good {"contact":{"employee":["Müller","Meier","Schulze"],"customer":"Good"}}
index=relations id=Bad {"contact":{"employee":"Jones","customer":"Bad"}}
Note how the employee column is collapsed into a JSON array. The repeated occurrence of the _id column
controls how values are folded into arrays for making use of the Elasticsearch JSON data model. Make sure your SQL query is ordered by _id.
In SQL, each column may be labeled. This label is used by the JDBC importer for JSON document construction. The dot is the path separator for the document strcuture.
For example
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"user" : "",
"password" : "",
"sql" : "select products.name as \"product.name\", orders.customer as \"product.customer.name\", orders.quantity * products.price as \"product.customer.bill\" from products, orders where products.name = orders.product"
}
}
the labeled columns are product.name, product.customer.name, and product.customer.bill.
A data example:
mysql> select products.name as "product.name", orders.customer as "product.customer", orders.quantity * products.price as "product.customer.bill" from products, orders where products.name = orders.product ;
+--------------+------------------+-----------------------+
| product.name | product.customer | product.customer.bill |
+--------------+------------------+-----------------------+
| Apples | Big | 1 |
| Bananas | Large | 2 |
| Oranges | Huge | 6 |
| Apples | Good | 2 |
| Oranges | Bad | 9 |
+--------------+------------------+-----------------------+
5 rows in set, 5 warnings (0.00 sec)
The structured objects constructed from these columns are
id=0 {"product":{"name":"Apples","customer":{"bill":1.0,"name":"Big"}}}
id=1 {"product":{"name":"Bananas","customer":{"bill":2.0,"name":"Large"}}}
id=2 {"product":{"name":"Oranges","customer":{"bill":6.0,"name":"Huge"}}}
id=3 {"product":{"name":"Apples","customer":{"bill":2.0,"name":"Good"}}}
id=4 {"product":{"name":"Oranges","customer":{"bill":9.0,"name":"Bad"}}}
There are column labels with an underscore as prefix that are mapped to special Elasticsearch document parameters for indexing:
_index the index this object should be indexed into
_type the type this object should be indexed into
_id the id of this object
_version the version of this object
_parent the parent of this object
_ttl the time-to-live of this object
_routing the routing of this object
See also
http://www.elasticsearch.org/guide/reference/mapping/parent-field.html
http://www.elasticsearch.org/guide/reference/mapping/ttl-field.html
http://www.elasticsearch.org/guide/reference/mapping/routing-field.html
When construction JSON documents, it is often the case you want to group SQL columns into a JSON object and line them up into JSON arrays. For allowing this, a bracket notation is used to identify children elements that repeat in each child.
Note, because of limitations in identifying SQL column groups, nested document structures may lead to repetitions of the same group. Fortunately, this is harmless to Elasticsearch queries.
Example:
| _id | blog.name | blog.published | blog.association[id] | blog.association[name] | blog.attachment[id] | blog.attachment[name] |
|---|---|---|---|---|---|---|
| 4679 | Joe | 2014-01-06 00:00:00 | 3917 | John | 9450 | /web/q/g/h/57436356.jpg |
| 4679 | Joe | 2014-01-06 00:00:00 | 3917 | John | 9965 | /web/i/s/q/GS3193626.jpg |
| 4679 | Joe | 2014-01-06 00:00:00 | 3917 | John | 9451 | /web/i/s/q/GS3193626.jpg |
Result:
{
"blog" : {
"attachment": [
{
"name" : "/web/q/g/h/57436356.jpg",
"id" : "9450"
},
{
"name" : "/web/i/s/q/GS3193626.jpg",
"id" : "9965"
},
{
"name" : "/web/i/s/q/GS3193626.jpg",
"id" : "9451"
}
],
"name" : "Joe",
"association" : [
{
"name" : "John",
"id" : "3917"
},
{
"name" : "John",
"id" : "3917"
},
{
"name" : "John",
"id" : "3917"
}
],
"published":"2014-01-06 00:00:00"
}
}
For fetching a table, a "select *" (star) query can be used.
Star queries are the simplest variant of selecting data from a database.
They dump tables into Elasticsearch row-by-row. If no _id column name is given, IDs will be automatically generated.
For example
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"user" : "",
"password" : "",
"sql" : "select * from orders"
}
}
and this table
mysql> select * from orders;
+----------+-----------------+---------+----------+---------------------+
| customer | department | product | quantity | created |
+----------+-----------------+---------+----------+---------------------+
| Big | American Fruits | Apples | 1 | 0000-00-00 00:00:00 |
| Large | German Fruits | Bananas | 1 | 0000-00-00 00:00:00 |
| Huge | German Fruits | Oranges | 2 | 0000-00-00 00:00:00 |
| Good | German Fruits | Apples | 2 | 2012-06-01 00:00:00 |
| Bad | English Fruits | Oranges | 3 | 2012-06-01 00:00:00 |
+----------+-----------------+---------+----------+---------------------+
5 rows in set (0.00 sec)
will result into the following JSON documents
id=<random> {"product":"Apples","created":null,"department":"American Fruits","quantity":1,"customer":"Big"}
id=<random> {"product":"Bananas","created":null,"department":"German Fruits","quantity":1,"customer":"Large"}
id=<random> {"product":"Oranges","created":null,"department":"German Fruits","quantity":2,"customer":"Huge"}
id=<random> {"product":"Apples","created":1338501600000,"department":"German Fruits","quantity":2,"customer":"Good"}
id=<random> {"product":"Oranges","created":1338501600000,"department":"English Fruits","quantity":3,"customer":"Bad"}
The JDBC importer allows to write data into the database for maintenance purpose.
Writing back data into the database makes sense for acknowledging fetched data.
Example:
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"user" : "",
"password" : "",
"sql" : [
{
"statement" : "select * from \"products\""
},
{
"statement" : "delete from \"products\" where \"_job\" = ?",
"parameter" : [ "$job" ]
}
],
"index" : "my_jdbc_index",
"type" : "my_jdbc_type"
}
}
In this example, the DB administrator has prepared product rows and attached a _job column to it
to enumerate the product updates incrementally. The assertion is that Elasticsearch should
delete all products from the database after they are indexed successfully. The parameter $job
is a counter. The importer state is saved in a file, so the counter is persisted.
It is recommended to use timestamps in UTC for synchronization. This example fetches
all product rows which has added since the last run, using a millisecond resolution
column mytimestamp:
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"statefile" : "statefile.json",
"user" : "",
"password" : "",
"sql" : [
{
"statement" : "select * from products where mytimestamp > ?",
"parameter" : [ "$metrics.lastexecutionstart" ]
}
],
"index" : "my_jdbc_index",
"type" : "my_jdbc_type"
}
}
the first time you run the script, it will generate the statefile.json file like this
{
"type" : "jdbc",
"jdbc" : {
"password" : "",
"index" : "my_jdbc_index",
"statefile" : "statefile.json",
"metrics" : {
"lastexecutionstart" : "2016-03-27T06:37:09.165Z",
"lastexecutionend" : "2016-03-27T06:37:09.501Z",
"counter" : "1"
},
"type" : "my_jdbc_type",
"user" : "",
"url" : "jdbc:mysql://localhost:3306/test",
"sql" : [ {
"statement" : "select * from products where mytimestamp > ?",
"parameter" : [ "$metrics.lastexecutionstart" ]
} ]
}
}
after this, you can select incremental data from table.
Stored procedures can also be used for fetchng data, like this example fo MySQL illustrates. See also Using Stored Procedures from where the example is taken.
create procedure GET_SUPPLIER_OF_COFFEE(
IN coffeeName varchar(32),
OUT supplierName varchar(40))
begin
select SUPPLIERS.SUP_NAME into supplierName
from SUPPLIERS, COFFEES
where SUPPLIERS.SUP_ID = COFFEES.SUP_ID
and coffeeName = COFFEES.COF_NAME;
select supplierName;
end
Now it is possible to call the procedure from the JDBC importer and index the result in Elasticsearch.
{
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"user" : "",
"password" : "",
"sql" : [
{
"callable" : true,
"statement" : "{call GET_SUPPLIER_OF_COFFEE(?,?)}",
"parameter" : [
"Colombian"
],
"register" : {
"mySupplierName" : { "pos" : 2, "type" : "varchar" }
}
}
],
"index" : "my_jdbc_index",
"type" : "my_jdbc_type"
}
}
Note, the parameter lists the input parameters in the order they should be applied, like in an
ordinary statement. The register declares a list of output parameters in the particular order
the pos number indicates. It is required to declare the JDBC type in the type attribute.
mySupplierName, the key of the output parameter, is used as the Elasticsearch field name specification,
like the column name specification in an ordinary SQL statement, because column names are not available
in callable statement result sets.
If there is more than one result sets returned by a callable statement, the JDBC importer enters a loop and iterates through all result sets.
Importing from a CSV is easy because a CSV JDBC driver is included.
Try something like this
{
"type" : "jdbc",
"jdbc" : {
"driver" : "org.xbib.jdbc.csv.CsvDriver",
"url" : "jdbc:xbib:csv:mydatadir?columnTypes=&separator=,",
"user" : "",
"password" : "",
"sql" : "select * from mycsvfile"
}
}
where
mydatadir - path to the directory where the CSV file exists
mycsvfile - the name of the file
columnTypes - column types will be inferred. Default is String, where column types will be all set to string
separator - the column separator
For a full list of the CSV JDBC driver options, see https://github.com/jprante/jdbc-driver-csv
The JDBC importer writes the state after each execution step into a state file which can be set by the
parameter statefile, see above in the parameter documentation. Default setting is not writing
to state file.
Example:
"sql" : ...,
"statefile" : "statefile.json",
...
You can use the statefile as input for a next JDBC importer invocation, once it is saved.
This is useful if you have to restart the JDBC importer. Because the statefile is written
in prettified JSON, it is also possible to adjust the
settings in the statefile if you need to synchronize with the JDBC source.
Note: there must be enough space on disk to write the state file. If disk is full, JDBC importer will write zero length files and give error messages in the importer log.
Metrics logging can be enabled to watch for the current transfer statistics.
Example:
"sql" : ...,
"schedule" : ...,
"statefile" : "statefile.json",
"metrics" : {
"enabled" : true,
"interval" : "1m",
"logger" : {
"plain" : false,
"json" : true
}
}
This configuration enables metrics logging, sets the metrics logging interval to one minute, and switches form plain loggin to JSON logging.
In the log4j2.xml configuration file, you can set up how to log. The loggers used for metrics logging are
metrics.source.plain - for plain format logging of the source
metrics.sink.plain - for plain format logging of the sink
metrics.source.json - for JSON format logging of the source
metrics.sink.json - for JSON format logging of the sink
See also the parameter documentation above.
The JDBC importer consists of three conceptual interfaces than can be implemented separately.
When you use the strategy parameter, the JDBC importer tries to load additional classes before
falling back to the standard strategy.
You can implement your own strategy by adding your implementation jars to the lib folder and
declaring the implementing classes in the META-INF/services directory.
So, it is easy to reuse or replace existing code, or adapt your own JDBC retrieval strategy to the unmodified JDBC importer jar.
The Source models the data producing side. Beside defining the JDBC connect parameters,
it manages a dual-channel connection to the data producer for reading and for writing.
The reading channel is used for fetching data, while the writing channel can update the source.
The Source API can be inspected at
http://jprante.github.io/elasticsearch-jdbc/apidocs/org/xbib/elasticsearch/jdbc/strategy/Source.html
The Sink is the abstraction of the destination where all the data is flowing from the source.
It controls the resource usage of the bulk indexing method of Elasticsearch. T
hrottling is possible by limiting the number of bulk actions per request or by the
maximum number of concurrent request.
The Sink API can be inspected at
http://jprante.github.io/elasticsearch-jdbc/apidocs/org/xbib/elasticsearch/jdbc/strategy/Sink.html
The Context is the abstraction to the thread which performs data fetching from the source
and transports it to the mouth. A 'move' is considered a single step in the execution cycle.
The Context API can be inspected at
http://jprante.github.io/elasticsearch-jdbc/apidocs/org/xbib/elasticsearch/jdbc/strategy/Context.html
The JDBC importer can be configured for different methods of data transport. Such methods of data transports are called a 'strategy'.
By default, the JDBC importer implements a standard strategy.
The standard strategy contains the following steps of processing:
- fetch data from the JDBC connection
- build structured objects and move them to Elasticsearch for indexing or deleting
In the sql parameter, a series of SQL statements can be defined which are executed to fetch the data.
If you want to extend the JDBC importer, for example by your custom password authentication, you could
extend org.xbib.elasticsearch.jdbc.strategy.standard.StandardSource.
Then, declare your strategy classes in META-INF/services. Add your
jar to the classpath and add the strategy parameter to the specifications.
-
Install PostgreSQL
Example: PostgreSQL .dmg (Version 9.1.5) for Mac OS X from http://www.enterprisedb.com/products-services-training/pgdownload
Filename: postgresql-9.1.5-1-osx.dmg
-
Install Elasticsearch
Follow instructions on https://www.elastic.co/products/elasticsearch
-
Install JDBC importer
wget http://xbib.org/repository/org/xbib/elasticsearch/importer/elasticsearch-jdbc/<version>/elasticsearch-jdbc-<version>-dist.zip(update version respectively)
-
Download PostgreSQL JDBC driver
Check http://jdbc.postgresql.org/download.html
Current version is JDBC4 Postgresql Driver, Version 9.1-902
Filname postgresql-9.1-902.jdbc4.jar
-
Copy driver into lib folder
cp postgresql-9.1-902.jdbc4.jar $JDBC_IMPORTER_HOME/lib -
Start Elasticsearch
-
Start JDBC importer
This is just a basic example to a database
testwith userfredand passwordsecret. Use the port configured during PostgreSQL installation. The default is5432.bin=$JDBC_IMPORTER_HOME/bin lib=$JDBC_IMPORTER_HOME/lib echo '{ "type" : "jdbc", "jdbc" : { "url" : "jdbc:postgresql://localhost:5432/test", "user" : "fred", "password" : "secret", "sql" : "select * from orders", "index" : "myindex", "type" : "mytype" } }' | java \ -cp "${lib}/*" \ -Dlog4j.configurationFile=${bin}/log4j2.xml \ org.xbib.tools.Runner \ org.xbib.tools.JDBCImporter -
Check log messages
In case the user does not exist, Elasticsearch will log a message.
-
Download Elasticsearch
-
Install Elasticsearch
Follow instructions on https://www.elastic.co/products/elasticsearch
-
Install JDBC importer
wget http://xbib.org/repository/org/xbib/elasticsearch/importer/elasticsearch-jdbc/<version>/elasticsearch-jdbc-<version>-dist.zip(update version respectively)
-
Download SQL Server JDBC driver from the vendor
-
Copy driver into lib folder
cp SQLJDBC4.jar $JDBC_IMPORTER_HOME/lib
-
Set up the database you want to be indexed. This includes allowing TCP/IP connections
-
Start Elasticsearch
./elasticsearch.bat -
Start JDBC importer
bin=$JDBC_IMPORTER_HOME/bin lib=$JDBC_IMPORTER_HOME/lib echo '{ "type" : "jdbc", "jdbc": { "url":"jdbc:sqlserver://localhost:1433;databaseName=ICFV", "user":"elasticsearch", "password":"elasticsearch", "sql":"select * from ScoreCards", "index" : "myindex", "type" : "mytype" } }' | java \ -cp "${lib}/*" \ -Dlog4j.configurationFile=${bin}/log4j2.xml \ org.xbib.tools.Runner \ org.xbib.tools.JDBCImporter -
You should see messages from the importer in the logfile.
-
install MySQL e.g. in /usr/local/mysql
-
start MySQL on localhost:3306 (default)
-
prepare a 'test' database in MySQL
-
create empty user '' with empty password '' (this user should exist as default user, otherwise set up a password and adapt the example)
-
execute SQL in "geo.dump" /usr/local/mysql/bin/mysql test < src/test/resources/geo.dump
-
then run this script
curl -XDELETE 'localhost:9200/myjdbc' bin=$JDBC_IMPORTER_HOME/bin lib=$JDBC_IMPORTER_HOME/lib echo ' { "type" : "jdbc", "jdbc" : { "url" : "jdbc:mysql://localhost:3306/test", "user" : "", "password" : "", "locale" : "en_US", "sql" : [ { "statement" : "select \"myjdbc\" as _index, \"mytype\" as _type, name as _id, city, zip, address, lat as \"location.lat\", lon as \"location.lon\" from geo" } ], "index" : "myjdbc", "type" : "mytype", "index_settings" : { "index" : { "number_of_shards" : 1 } }, "type_mapping": { "mytype" : { "properties" : { "location" : { "type" : "geo_point" } } } } } }' | java \ -cp "${lib}/*" \ -Dlog4j.configurationFile=${bin}/log4j2.xml \ org.xbib.tools.Runner \ org.xbib.tools.JDBCImporter echo "sleeping while importer should run..." sleep 10 curl -XGET 'localhost:9200/myjdbc/_refresh' curl -XPOST 'localhost:9200/myjdbc/_search?pretty' -d ' { "query": { "filtered": { "query": { "match_all": { } }, "filter": { "geo_distance" : { "distance" : "20km", "location" : { "lat" : 51.0, "lon" : 7.0 } } } } } }'
-
install Postgres and PostGIS
-
start Postgres on localhost:5432 (default)
-
prepare a 'test' database in Postgres, connect to the database using psql and create the PostGIS extension
CREATE EXTENSION POSTGIS -
create user 'test' with password 'test', quit psql
-
import geo table (includes geom field of type geometry) from "geo.sql" psql -U test -d test < src/test/resources/geo.sql
-
then run this script. IMPORTANT: note the use of explicit rounding and scale parameter, by default PostGIS will output floats, these will cause you problems in your geom_point in Elasticsearch unless you use specific casts, you have been warned!
curl -XDELETE 'localhost:9200/myjdbc' bin=$JDBC_IMPORTER_HOME/bin lib=$JDBC_IMPORTER_HOME/lib echo ' { "type" : "jdbc", "jdbc" : { "url" : "jdbc:postgres://localhost:5432/test", "user" : "test", "password" : "test", "locale" : "en_GB", "sql" : "select geonameid as _id, name, admin1_code, admin2_code, admin3_code, round(ST_Y(geom)::numeric,8) as \"location.lat\", round(ST_X(geom)::numeric,8) as \"location.lon\" from geo", "index" : "myjdbc", "type" : "mytype", "scale" : 8, "index_settings" : { "index" : { "number_of_shards" : 1 } }, "type_mapping": { "mytype" : { "properties" : { "location" : { "type" : "geo_point" } } } } } }' | java \ -cp "${lib}/*" \ -Dlog4j.configurationFile=${bin}/log4j2.xml \ org.xbib.tools.Runner \ org.xbib.tools.JDBCImporter echo "sleeping while importer should run..." sleep 10 curl -XGET 'localhost:9200/myjdbc/_refresh' curl -XPOST 'localhost:9200/myjdbc/_search?pretty' -d ' { "query": { "filtered": { "query": { "match_all": { } }, "filter": { "geo_distance" : { "distance" : "20km", "location" : { "lat" : 51.477347, "lon" : -0.000850 } } } } } }'
The JDBC importer understands WKT http://en.wikipedia.org/wiki/Well-known_text "POINT" and "POLYGON" formats and converts them to GeoJSON.
With MySQL, the astext function can format WKT from columns of type geometry.
Example:
mysql -u root test <<EOT
drop table if exists test.geom;
create table test.geom (
id integer,
g geometry
);
set @g = 'POLYGON((0 0,10 0,10 10,0 10,0 0),(5 5,7 5,7 7,5 7, 5 5))';
insert into test.geom values (0, GeomFromText(@g));
EOT
curl -XDELETE 'localhost:9200/myjdbc'
echo '
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:mysql://localhost:3306/test",
"user" : "",
"password" : "",
"locale" : "en_US",
"sql" : "select \"myjdbc\" as _index, \"mytype\" as _type, id as _id, astext(g) as polygon from geom",
"elasticsearch" : {
"cluster" : "elasticsearch",
"host" : "localhost",
"port" : 9300
},
"index" : "myjdbc",
"type" : "mytype",
"index_settings" : {
"index" : {
"number_of_shards" : 1
}
},
"type_mapping": {
"mytype" : {
"properties" : {
"polygon" : {
"type" : "geo_shape",
"tree" : "quadtree"
}
}
}
}
}
}
' | java \
-cp "${lib}/*" \
-Dlog4j.configurationFile=${bin}/log4j2.xml \
org.xbib.tools.Runner \
org.xbib.tools.JDBCImporter
Oracle imposes a 30 character limit on column name aliases. This makes it sometimes hard to define columns names for Elasticsearch field names. For this, a column name map can be used like this:
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:oracle:thin:@//localhost/sid",
"user" : "user",
"password" : "password",
"sql" : "select or_id as \"_id\", or_tan as \"o.t\", or_status as \"o.s\", stages.* from orders, stages where or_id = st_or_id and or_seqno = st_seqno",
"column_name_map" : {
"o" : "order",
"t" : "transaction_id",
"s" : "status"
}
}
}
For some JDBC drivers, advanced parameters can be passed that are not specified in the driver URL, but in the JDBC connection properties. You can specifiy connection properties like this:
{
"type" : "jdbc",
"jdbc" : {
"url" : "jdbc:oracle:thin:@//localhost:1521/sid",
"user" : "user",
"password" : "password",
"sql" : "select ... from ...",
"connection_properties" : {
"oracle.jdbc.TcpNoDelay" : false,
"useFetchSizeWithLongColumn" : false,
"oracle.net.CONNECT_TIMEOUT" : 10000,
"oracle.jdbc.ReadTimeout" : 50000
}
}
}
Elasticsearch JDBC Importer
Copyright (C) 2012-2015 Jörg Prante
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.