You should still use the same code. *import org.locationtech.jts.geom.Geometry*
Because Sedona Python Adapter packages JTS internally: https://github.com/apache/incubator-sedona/blob/master/python-adapter/pom.xml#L41 On Wed, Feb 24, 2021 at 6:10 AM Jerrod Wagnon <[email protected]> wrote: > Thank you for the follow up. Testing out those specific libraries, but > not sure where to source the Geometry type from now, since I’m not using > JTS anymore. > > > > *import org.locationtech.jts.geom.Geometry* > > > > command-4367909143506860:37: error: not found: type Geometry var pointRDD > = new SpatialRDD[Geometry]() ^ > > > > Thanks, > > > > Jerrod > > > > *From:* Jia Yu <[email protected]> > *Sent:* Tuesday, February 23, 2021 7:02 PM > *To:* Jerrod Wagnon <[email protected]>; [email protected] > *Subject:* Re: Attribute Columns Question > > > > *CAUTION:* This email originated from outside of the organization. Do not > click links or open attachments unless you recognize the sender and know > the content is safe. > > > > BTW, in fact, you just need to use > > > > sedona-python-adapter, > > sedona-viz, > > geotools-wrapper, > > sernetcdf. > > > > JTS / jts2geojson jars are not needed. Then the JTS / jts2geojson > conflict issue should be gone. > > > > On Tue, Feb 23, 2021 at 4:52 PM Jia Yu <[email protected]> wrote: > > Jerrod, > > > > Just updated the doc. We were struggling to write docs for all these > packaging issues in Sedona 1.0.0. The latest doc of Scala and Java > dependency should be super clear: > > > > You need to open this link in an incognito window which automatically > clears the old browser cache. > > > > > http://sedona.apache.org/download/GeoSpark-All-Modules-Maven-Central-Coordinates/#spark-30-scala-212 > <https://urldefense.proofpoint.com/v2/url?u=http-3A__sedona.apache.org_download_GeoSpark-2DAll-2DModules-2DMaven-2DCentral-2DCoordinates_-23spark-2D30-2Dscala-2D212&d=DwMFaQ&c=MsXLK6sQQBUgeD0JbTyYgA&r=9azZCQem_NPy1XE-fJ5d4mblFkmnSjMQGyiXFG3JznU&m=yywm82BF4_20l0Wnhj2fn25WixwguBJs4N63R46vOek&s=G5zHcJlsAriSFXIbGzDLHsuLfrD9mNuwY7rB0snDRo4&e=> > > > > On Tue, Feb 23, 2021 at 2:01 PM Jia Yu <[email protected]> wrote: > > Hi Jerrod, > > > > If you look into that code example, actually there is one more line before > it: import scala.collection.JavaConversions._ > > > > Just add this line, you will be fine. > > > > Sedona 1.0.0 has to use JTS 1.18. You may have other errors later on. > jts2geojson library, internally pacakge JTS 1.16. You will need to exclude > it probably: > https://github.com/bjornharrtell/jts2geojson/blob/master/pom.xml#L58 > <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_bjornharrtell_jts2geojson_blob_master_pom.xml-23L58&d=DwMFaQ&c=MsXLK6sQQBUgeD0JbTyYgA&r=9azZCQem_NPy1XE-fJ5d4mblFkmnSjMQGyiXFG3JznU&m=yywm82BF4_20l0Wnhj2fn25WixwguBJs4N63R46vOek&s=l8ibdWi9EcMR83IYx1gy0Og14K760xp7gL_jroIk85c&e=> > > > > We will update our docs shortly > > > > > > > > On Tue, Feb 23, 2021 at 1:35 PM Jerrod Wagnon <[email protected]> > wrote: > > Thanks. There was a conflict with that method and the rdd.fieldNames > (java list vs a sequence) when I tried to pass those in from the rdd. > > > > command-4367909143506860:54: error: type mismatch; found : > java.util.List[String] required: Seq[String] var joinResultTest = > Adapter.toDf(resultPairRDD, polygonRDD.fieldNames, pointRDD.fieldNames, > spark) > > > > However, I was able to create the sequences manually and it seems to be > working fine now. I had tried this previously, but had the geometry > columns included in the sequence, which isn’t needed. > > > > *var joinResultDf = Adapter.toDf(resultPairRDD, Seq("LocationCode"), > Seq("UniqueID","EquipmentID","MKT_AREA"), spark)* > > > > Also, in Databricks, I was having issues with the latest library for > locationtech. 1.18 was having a conflict with wololo, so I ended up just > loading 1.16 instead. That might not be a good solution, but it was the > only way I could get all the dependencies to load properly on cluster > restart. Just wanted to mention that in case anyone else runs into issues > with Databricks Spark 3.0.1 and Scala 2.12. Everything seems to be working > for my use cases with these libraries. > > > > > > Thanks again. Really appreciate the help and your quick response! > > > > Jerrod > > > > *From:* Jia Yu <[email protected]> > *Sent:* Tuesday, February 23, 2021 1:41 PM > *To:* [email protected]; Jerrod Wagnon <[email protected]> > *Subject:* Re: Attribute Columns Question > > > > *CAUTION:* This email originated from outside of the organization. Do not > click links or open attachments unless you recognize the sender and know > the content is safe. > > > > Hi Jerrod, > > > > Can you try to use this method: > https://github.com/apache/incubator-sedona/blob/master/sql/src/test/scala/org/apache/sedona/sql/adapterTestScala.scala#L138 > <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_incubator-2Dsedona_blob_master_sql_src_test_scala_org_apache_sedona_sql_adapterTestScala.scala-23L138&d=DwMFaQ&c=MsXLK6sQQBUgeD0JbTyYgA&r=9azZCQem_NPy1XE-fJ5d4mblFkmnSjMQGyiXFG3JznU&m=vZEjTroVnQm5j1w7ocpG9We1c_0EKseB4KvDXL9Noek&s=yHlmPPdrz1gjnP-gAHrZPweDJ_CSudJO0S3qRF3er6w&e=> > > > > Basically, you need to use rdd.fieldnames as input parameters. I think our > doc missed this part. > > > > Our Adapter implementation for join query result is here: > https://github.com/apache/incubator-sedona/blob/master/sql/src/main/scala/org/apache/sedona/sql/utils/Adapter.scala#L130 > <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_incubator-2Dsedona_blob_master_sql_src_main_scala_org_apache_sedona_sql_utils_Adapter.scala-23L130&d=DwMFaQ&c=MsXLK6sQQBUgeD0JbTyYgA&r=9azZCQem_NPy1XE-fJ5d4mblFkmnSjMQGyiXFG3JznU&m=vZEjTroVnQm5j1w7ocpG9We1c_0EKseB4KvDXL9Noek&s=o7FT3PmYqmPFJrSKmNt4doxdcub3uUq4oaOerIv8xd0&e=> > > > > Thanks, > > Jia > > > > On Tue, Feb 23, 2021 at 9:13 AM Jerrod Wagnon <[email protected]> > wrote: > > I’m sure this is something simple I’m missing. Caveat, I’m not a > developer, but can manage. > > > > Is there something different that needs to be done in Sedona vs the > previous Snapshot version for Spark 3.0 to get additional columns to carry > through in the JoinQuery.SpatialJoinQueryFlat results? Previously, I just > passed the columns in with the Adapter.toSpatialRDD and they carried > through. Now, I'm just just getting my two Geometry columns when > converting back to a dataframe. I've tried passing the left and right > field names into Adapter.toDf, but that results in an error when displaying > the resulting dataframe. I’m using Scala in Databricks. I’ve read the > online documentation, but can’t seem to find examples that help in this > scenario. > > > > *Sedona:* > > > > > > *Snapshot:* > > > > > > Thanks, > > > > *Jerrod* > > This email contains confidential material for the sole use of the intended > recipient(s). Any review, use, distribution, or disclosure by others is > strictly prohibited. If you are not the intended recipient (or authorized > to receive for the recipient), please contact the sender by reply email and > delete all copies of this message. > > This email contains confidential material for the sole use of the intended > recipient(s). Any review, use, distribution, or disclosure by others is > strictly prohibited. If you are not the intended recipient (or authorized > to receive for the recipient), please contact the sender by reply email and > delete all copies of this message. > > This email contains confidential material for the sole use of the intended > recipient(s). Any review, use, distribution, or disclosure by others is > strictly prohibited. If you are not the intended recipient (or authorized > to receive for the recipient), please contact the sender by reply email and > delete all copies of this message. >
