Globally Cached Lookups
Globally cached lookups are appropriate for lookups which are not possible to pass at query time due to their size, or are not desired to be passed at query time because the data is to reside in and be handled by the Druid servers, and are small enough to reasonably populate in-memory. This usually means tens to tens of thousands of entries per lookup.
Globally cached lookups all draw from the same cache pool, allowing each process to have a fixed cache pool that can be used by cached lookups.
Globally cached lookups can be specified as part of the as a type of cachedNamespace
{
"type": "cachedNamespace",
"extractionNamespace": {
"type": "jdbc",
"connectorConfig": {
"connectURI": "jdbc:mysql:\/\/localhost:3306\/druid",
"user": "druid",
"password": "diurd"
},
"table": "lookupTable",
"keyColumn": "mykeyColumn",
"valueColumn": "myValueColumn",
"filter" : "myFilterSQL (Where clause statement e.g LOOKUPTYPE=1)",
"tsColumn": "timeColumn"
},
"firstCacheTimeout": 120000,
"injective":true
}
The parameters are as follows
If firstCacheTimeout
is set to a non-zero value, it should be less than druid.manager.lookups.hostUpdateTimeout
. If firstCacheTimeout
is NOT set, then management is essentially asynchronous and does not know if a lookup succeeded or failed in starting. In such a case logs from the processes using lookups should be monitored for repeated failures.
Proper functionality of globally cached lookups requires the following extension to be loaded on the Broker, Peon, and Historical processes: druid-lookups-cached-global
In a simple case where only one exists (realtime_customer2
) with one cachedNamespace
lookup called country_code
, the resulting configuration JSON looks similar to the following:
{
"realtime_customer2": {
"country_code": {
"version": "v0",
"lookupExtractorFactory": {
"type": "cachedNamespace",
"extractionNamespace": {
"type": "jdbc",
"connectorConfig": {
"connectURI": "jdbc:mysql:\/\/localhost:3306\/druid",
"user": "druid",
"password": "diurd"
},
"table": "lookupValues",
"valueColumn": "value_text",
"filter": "value_type='country'",
"tsColumn": "timeColumn"
},
"firstCacheTimeout": 120000,
"injective": true
}
}
}
}
Where the Coordinator endpoint /druid/coordinator/v1/lookups/realtime_customer2/country_code
should return
{
"version": "v0",
"lookupExtractorFactory": {
"type": "cachedNamespace",
"extractionNamespace": {
"type": "jdbc",
"connectorConfig": {
"connectURI": "jdbc:mysql://localhost:3306/druid",
"user": "druid",
"password": "diurd"
},
"table": "lookupValues",
"keyColumn": "value_id",
"valueColumn": "value_text",
"filter": "value_type='country'",
},
"firstCacheTimeout": 120000,
"injective": true
}
}
Lookups are cached locally on Historical processes. The following are settings used by the processes which service queries when setting namespaces (Broker, Peon, Historical)
Property | Description | Default |
---|---|---|
druid.lookup.namespace.cache.type | Specifies the type of caching to be used by the namespaces. May be one of [offHeap , onHeap ]. offHeap uses a temporary file for off-heap storage of the namespace (memory mapped files). onHeap stores all cache on the heap in standard java map types. | onHeap |
druid.lookup.namespace.numExtractionThreads | The number of threads in the thread pool dedicated for lookup extraction and updates. This number may need to be scaled up, if you have a lot of lookups and they take long time to extract, to avoid timeouts. | 2 |
druid.lookup.namespace.numBufferedEntries | If using off-heap caching, the number of records to be stored on an on-heap buffer. | 100,000 |
onHeap
uses ConcurrentMap
s in the java heap, and thus affects garbage collection and heap sizing. offHeap
uses an on-heap buffer and MapDB using memory-mapped files in the java temporary directory. So if total number of entries in the cachedNamespace
is in excess of the buffer’s configured capacity, the extra will be kept in memory as page cache, and paged in and out by general OS tunings. It’s highly recommended that druid.lookup.namespace.numBufferedEntries
is set when using offHeap
, the value should be chosen from the range between 10% and 50% of the number of entries in the lookup.
For additional lookups, please see our extensions list.
The remapping values for each globally cached lookup can be specified by a JSON object as per the following examples:
{
"type":"uri",
"uri": "s3://bucket/some/key/prefix/renames-0003.gz",
"namespaceParseSpec":{
"format":"csv",
"columns":[
"[\"key\"",
"\"value\"]"
]
},
"pollPeriod":"PT5M"
}
One of either uri
or uriPrefix
must be specified, as either a local file system (file://), HDFS (hdfs://), S3 (s3://) or GCS (gs://) location. HTTP location is not currently supported.
The pollPeriod
value specifies the period in ISO 8601 format between checks for replacement data for the lookup. If the source of the lookup is capable of providing a timestamp, the lookup will only be updated if it has changed since the prior tick of pollPeriod
. A value of 0, an absent parameter, or null
all mean populate once and do not attempt to look for new data later. Whenever an poll occurs, the updating system will look for a file with the most recent timestamp and assume that one with the most recent data set, replacing the local cache of the lookup data.
The namespaceParseSpec
can be one of a number of values. Each of the examples below would rename foo to bar, baz to bat, and buck to truck. All parseSpec types assumes each input is delimited by a new line. See below for the types of parseSpec supported.
Only ONE file which matches the search will be used. For most implementations, the discriminator for choosing the URIs is by whichever one reports the most recent timestamp for its modification time.
csv lookupParseSpec
Parameter | Description | Required | Default |
---|---|---|---|
columns | The list of columns in the csv file | no if hasHeaderRow is set | null |
keyColumn | The name of the column containing the key | no | The first column |
valueColumn | The name of the column containing the value | no | The second column |
hasHeaderRow | A flag to indicate that column information can be extracted from the input files’ header row | no | false |
skipHeaderRows | Number of header rows to be skipped | no | 0 |
If both skipHeaderRows
and hasHeaderRow
options are set, skipHeaderRows
is first applied. For example, if you set skipHeaderRows
to 2 and hasHeaderRow
to true, Druid will skip the first two lines and then extract column information from the third line.
example input
bat,something2,baz
truck,something3,buck
example namespaceParseSpec
"namespaceParseSpec": {
"format": "csv",
"columns": ["value","somethingElse","key"],
"keyColumn": "key",
"valueColumn": "value"
}
tsv lookupParseSpec
example input
bar|something,1|foo
bat|something,2|baz
truck|something,3|buck
example namespaceParseSpec
"namespaceParseSpec": {
"format": "tsv",
"columns": ["value","somethingElse","key"],
"keyColumn": "key",
"valueColumn": "value",
"delimiter": "|"
}
customJson lookupParseSpec
Parameter | Description | Required | Default |
---|---|---|---|
keyFieldName | The field name of the key | yes | null |
valueFieldName | The field name of the value | yes | null |
example input
example namespaceParseSpec
"namespaceParseSpec": {
"format": "customJson",
"keyFieldName": "key",
"valueFieldName": "value"
}
With customJson parsing, if the value field for a particular row is missing or null then that line will be skipped, and will not be included in the lookup.
simpleJson lookupParseSpec
The simpleJson
lookupParseSpec does not take any parameters. It is simply a line delimited JSON file where the field is the key, and the field’s value is the value.
example input
{"foo": "bar"}
{"baz": "bat"}
{"buck": "truck"}
example namespaceParseSpec
"namespaceParseSpec":{
"format": "simpleJson"
}
JDBC lookup
The JDBC lookups will poll a database to populate its local cache. If the tsColumn
is set it must be able to accept comparisons in the format '2015-01-01 00:00:00'
. For example, the following must be valid SQL for the table SELECT * FROM some_lookup_table WHERE timestamp_column > '2015-01-01 00:00:00'
. If tsColumn
is set, the caching service will attempt to only poll values that were written after the last sync. If tsColumn
is not set, the entire table is pulled every time.
{
"type":"jdbc",
"connectorConfig":{
"connectURI":"jdbc:mysql://localhost:3306/druid",
"user":"druid",
"password":"diurd"
},
"table":"some_lookup_table",
"keyColumn":"the_old_dim_value",
"valueColumn":"the_new_dim_value",
"tsColumn":"timestamp_column",
"pollPeriod":600000,
"maxHeapPercentage": 10
}
If using JDBC, you will need to add your database’s client JAR files to the extension’s directory. For Postgres, the connector JAR is already included. See the MySQL extension documentation for instructions to obtain or MariaDB connector libraries. The connector JAR should reside in the classpath of Druid’s main class loader. To add the connector JAR to the classpath, you can copy the downloaded file to
lib/
under the distribution root directory. Alternatively, create a symbolic link to the connector in thelib
directory.