ExternalVendorCode/corda
1
0
Fork 0
mirror of https://github.com/corda/corda.git synced 2024-12-20 21:43:14 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Extracted a tool class to reduce duplicated logic between InMemoryIdentityService and PersistentIdentityService (#3141)

Browse Source
This commit is contained in:
renlulu 2018-05-15 01:00:07 +08:00 committed by Joel Dudley
parent e1dc57ba9d
commit b2e9a427a8
3 changed files with 26 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
node/src/main/kotlin/net/corda/node/services/identity/IdentityServiceUtil.kt Normal file
View File

@ -0,0 +1,24 @@
package net.corda.node.services.identity
import net.corda.core.identity.CordaX500Name
import net.corda.core.identity.Party
fun partiesFromName(query: String, exactMatch: Boolean, x500name: CordaX500Name, results: LinkedHashSet<Party>, party: Party) {
val components = listOfNotNull(x500name.commonName, x500name.organisationUnit, x500name.organisation, x500name.locality, x500name.state, x500name.country)
components.forEach { component ->
if (exactMatch && component == query) {
results += party
} else if (!exactMatch) {
// We can imagine this being a query over a lucene index in future.
//
// Kostas says: We can easily use the Jaro-Winkler distance metric as it is best suited for short
// strings such as entity/company names, and to detect small typos. We can also apply it for city
// or any keyword related search in lists of records (not raw text - for raw text we need indexing)
// and we can return results in hierarchical order (based on normalised String similarity 0.0-1.0).
if (component.contains(query, ignoreCase = true))
results += party
}
}
}

17
node/src/main/kotlin/net/corda/node/services/identity/InMemoryIdentityService.kt
View File

@ -103,22 +103,7 @@ class InMemoryIdentityService(identities: Array<out PartyAndCertificate>,
override fun partiesFromName(query: String, exactMatch: Boolean): Set<Party> { override fun partiesFromName(query: String, exactMatch: Boolean): Set<Party> {
val results = LinkedHashSet<Party>() val results = LinkedHashSet<Party>()
for ((x500name, partyAndCertificate) in principalToParties) { for ((x500name, partyAndCertificate) in principalToParties) {
val party = partyAndCertificate.party partiesFromName(query, exactMatch, x500name, results, partyAndCertificate.party)
val components = listOfNotNull(x500name.commonName, x500name.organisationUnit, x500name.organisation, x500name.locality, x500name.state, x500name.country)
components.forEach { component ->
if (exactMatch && component == query) {
results += party
} else if (!exactMatch) {
// We can imagine this being a query over a lucene index in future.
//
// Kostas says: We can easily use the Jaro-Winkler distance metric as it is best suited for short
// strings such as entity/company names, and to detect small typos. We can also apply it for city
// or any keyword related search in lists of records (not raw text - for raw text we need indexing)
// and we can return results in hierarchical order (based on normalised String similarity 0.0-1.0).
if (component.contains(query, ignoreCase = true))
results += party
}
}
} }
return results return results
} }

17
node/src/main/kotlin/net/corda/node/services/identity/PersistentIdentityService.kt
View File

@ -180,22 +180,7 @@ class PersistentIdentityService(override val trustRoot: X509Certificate,
override fun partiesFromName(query: String, exactMatch: Boolean): Set<Party> { override fun partiesFromName(query: String, exactMatch: Boolean): Set<Party> {
val results = LinkedHashSet<Party>() val results = LinkedHashSet<Party>()
for ((x500name, partyId) in principalToParties.allPersisted()) { for ((x500name, partyId) in principalToParties.allPersisted()) {
val party = keyToParties[partyId]!!.party partiesFromName(query, exactMatch, x500name, results, keyToParties[partyId]!!.party)
val components = listOfNotNull(x500name.commonName, x500name.organisationUnit, x500name.organisation, x500name.locality, x500name.state, x500name.country)
components.forEach { component ->
if (exactMatch && component == query) {
results += party
} else if (!exactMatch) {
// We can imagine this being a query over a lucene index in future.
//
// Kostas says: We can easily use the Jaro-Winkler distance metric as it is best suited for short
// strings such as entity/company names, and to detect small typos. We can also apply it for city
// or any keyword related search in lists of records (not raw text - for raw text we need indexing)
// and we can return results in hierarchical order (based on normalised String similarity 0.0-1.0).
if (component.contains(query, ignoreCase = true))
results += party
}
}
} }
return results return results
} }