#1 Browsing by Contributors using dedicated Elasticsearch index
This approach is the same as browsing by contributors, but with a few extensions.
Pros | Cons |
---|---|
Requires only Elasticsearch and Kafka | Requires additional space to store the dedicated index with all linked instance ids |
Requires integration with kafka to prevent race conditions and get rid of optimistic locks on Elasticsearch documents | |
Requires additional code to manage update and delete events |
Approach is contains a several steps to implement:
- Send contributor events from instance to the search.instance-contributor topic (message key = sha1 hash of concatenatination - tenantId + contributorType + key)
- Implement a new Kafka Listener to listen contributor only events (Kafka will arrange contributors with the same key in the same partition, allowing event processing without optimistic locking)
- Implement a new InstanceContributor repository with following upsert request for events groups
- Implement a query which allows to find contributor exact by name and type (it can be CQL with double equal sign, but it looks like that John and John. are threated as the same value (ES standard tokenizer))
Upsert query example:
{ "script": { "source": "def set=new LinkedHashSet(ctx._source.instances);set.addAll(params.ins);params.del.forEach(set::remove);ctx._source.instances=set", "lang": "painless", "params": { "ins": [ "instanceId#1|type#1", "instanceId#2|type#1", "instanceId#3|type#1" ], "del": [ "instanceId#4|type#1"] } }, "upsert": { "id": "abshc", "name": "Antoniou, Grigoris", "contributorTypeId": "type#1", "instances": [ "instanceId#1|type#1", "instanceId#2|type#1", "instanceId#3|type#1" ] } }
#2 Browsing by Contributors using PostgreSQL table
This options provides ability to browse by using PostgreSQL table with index on the contributors field.
create table instance_contributor ( contributor text not null, instance_id text not null, constraint instance_contributors_pk primary key (contributor, instance_id) ); create index instance_contributors_contributor on diku_mod_search.instance_contributors (lower(contributor));
Insertions can be done in batch, which can be done configuring Spring Data Jpa:
insert into instance_contributor(instance_id, contributor) values (?,?) on conflict do nothing;
spring: jpa: properties: hibernate: order_inserts: true order_updates: true jdbc.batch_size: 500
@Data @Entity @NoArgsConstructor @Table(name = "instance_contributors") @AllArgsConstructor(staticName = "of") @SQLInsert(sql = "insert into instance_contributors(instance_id, contributor) values (?, ?) on conflict do nothing") public class InstanceContributorEntity implements Persistable<InstanceContributorEntityId> { @EmbeddedId private InstanceContributorEntityId id; @Override public boolean isNew() { return true; } } @Data @Embeddable @NoArgsConstructor @AllArgsConstructor(staticName = "of") public class InstancecontributorEntityId implements Serializable { private String contributor; private String instanceId; }
select contributor, count(*) from instance_contributors where contributor in ( select distinct on (lower(contributor)) contributor from instance_contributors where lower(contributor) < :anchor order by lower(contributor) desc limit :limit ) group by contributor order by lower(contributor);
select contributor, count(*) from instance_contributors where contributor in ( select distinct on (lower(contributor)) contributor from instance_contributors where lower(contributor) >= :anchor order by lower(contributor) limit :limit ) group by contributor order by lower(contributor);
Pros | Cons |
---|---|
Fast to query (faster than other options) | Requires additional space to store the dedicated index (~1Gb per million resources) |
Easy to manage update and delete events |
#3 Browsing by Contributors using PostgreSQL index and Elasticsearch terms aggregation
This approach can be implemented in two steps:
- Create new index for lowercase contributor values from instance jsons and browse by it
- Retrieve counts using terms aggregation per contributor entity
Pros | Cons |
---|---|
It can be slightly better than option #1, because there is no need to store and manage dedicated index or table | Additional load to the existing data storage and mod-inventory-storage |
No need to manage update and delete events | Additional index can slow down document indexing for mod-inventory-storage |
Slower than option #2 |
#4 Browse by range query and numeric representation of incoming value
This approach can be based on the Call-Number browsing. The main idea is to create a long value for the string and use it for the range query to limit the number of documents to aggregate.
Items to check/investigate:
- Retrieve number of instances per each contributor
- Manage how to deal with redundant contributors in the result of terms aggregation (script query?)
Pros | Cons |
---|---|
Approximately, the same performance as Call-Number Browsing. | Additional value must be stored within each document - numeric value for each contributor |
No need to store dedicated Elasticsearch index, PostgreSQL table or index | There is no way to collect facets for contributor type/name |
Filter contributors by type will be hard too | |
In case of large collisions (2000-3000 resources per contributr) - response will be slow |