adding class to grouped_options_for_select in Rails 4 form - css

I can't seem to get the class on this select to work. The grouped collection works...but not the class:
= f.select :topic_id, grouped_options_for_select([['News', #topics.news.order(title: :asc).collect {|v| [ v.title, v.id ] }],
['Opinion', #topics.opinion.order(title: :asc).collect {|v| [ v.title, v.id ] }]]), html: {include_blank: false , id: 'page_topic', class: 'form-control'}


Try without html: key:
= f.select :topic_id, grouped_options_for_select([['News', #topics.news.order(title: :asc).collect {|v| [ v.title, v.id ] }],
['Opinion', #topics.opinion.order(title: :asc).collect {|v| [ v.title, v.id ] }]]), include_blank: false, id: 'page_topic', class: 'form-control'
P.S. Your code is very hard to read, try to extract some parts into variables, like this:
- news_options = ['News', #topics.news.order(title: :asc).collect {|v| [ v.title, v.id ] } ]
- opinion_options = ['Opinion', #topics.opinion.order(title: :asc).collect {|v| [ v.title, v.id ] }]
- options = grouped_options_for_select([news_options, opinion_options])
= f.select :topic_id, options, include_blank: false, id: 'page_topic', class: 'form-control'

Related

jq | return subset of data based on matched array element

I am trying to parse the below json file using jq. I'd like to return a subset of that data.
{
"imageDetails": [
{
"imageSizeInBytes": 2066961,
"imageDigest": "sha256:ff089cca12407adce49e70771a32c2fd48d53d8d3fb735cee430037e6b27c535123",
"imageTags": [
"test",
"latest"
],
"registryId": "98654010662132",
"repositoryName": "testrepository",
"imagePushedAt": 1520462286
},
{
"imageSizeInBytes": 2066961,
"imageDigest": "sha256:c30974de3423250382f5b33be01103c11de73459ceaedd789dff9b461532eb2dd55",
"imageTags": [
"test3"
],
"registryId": "98654010662132",
"repositoryName": "testrepository",
"imagePushedAt": 1520462305
},
{
"imageSizeInBytes": 2066961,
"imageDigest": "sha256:05048eb6cc559e953e6c98afa0e71fd836c37a34563531487af2b5d1c9430570ff9",
"imageTags": [
"mytest1",
"mytest2",
"mytest3"
],
"registryId": "98654010662132",
"repositoryName": "testrepository",
"imagePushedAt": 1520459886
},
{
"imageSizeInBytes": 2066961,
"imageDigest": "sha256:bf23ada8a14d1c70c476b4f39b9b849fd5191d8fc419312b3d1b8f59aa182b050e6",
"imageTags": [
"test3-latest"
],
"registryId": "98654010662132",
"repositoryName": "testrepository",
"imagePushedAt": 1520462305
},
{
"imageSizeInBytes": 2066961,
"imageDigest": "sha256:a040f335556e783930905d0b7bd9f55f1400a2778e39983c3d1e3cc41643c090ee0",
"imageTags": [
"test2"
],
"registryId": "98654010662132",
"repositoryName": "testrepository",
"imagePushedAt": 1520462304
}
]
}
Specifically, I'd like to search for 'imageTags' where an item in the array is "mytest1". Returning output should be the dictionary block. Like this:
{
"imageSizeInBytes": 2066961,
"imageDigest": "sha256:05048eb6cc559e953e6c98afa0e71fd836c37a34563531487af2b5d1c9430570ff9",
"imageTags": [
"mytest1",
"mytest2",
"mytest3"
],
"registryId": "98654010662132",
"repositoryName": "testrepository",
"imagePushedAt": 1520459886
}
Any assistance would be greatly appreciated.

The key to a simple formulation of your query is any/2:
.imageDetails[] | select(any(.imageTags[]; . == "mytest1"))

Google Maps API and GeoJSON: How do I create different colored lines with unique properties and infowindows

I am having a problem with GeoJSON and Google Maps API.
How do I plot a MultiLineString with ...
1) Each line having It's own color and
2) Each line having it's own properties and
3) Each line should be clickable and show an info window with it's properties
Samples Javascript:
var dataGEOJSON=[];
function LoadMyGEOJSON(key)
{
dataGEOJSON[key] = new google.maps.Data();
dataGEOJSON[key].loadGeoJson('GetLatLngGEOJSON.aspx?key=' + key);
dataGEOJSON[key].setMap(map);
}
The reason for the array, is that way I can keep track of the keys they loaded, as the user can load or unload the keys from the map in their user interface.
Sample GeoJSON:
{
"type":"FeatureCollection",
"features":[
{
"type":"Feature",
"properties":{
"Key":"007",
"Line1":"<this is line 1 desc>",
"Line2":"<this is line 2 desc>",
"Line3":"<this is line 3 desc>",
"Line4":"<this is line 4 desc>",
"Line5":"<this is line 5 desc>",
"Line6":"<this is line 6 desc>",
"Line7":"<this is line 7 desc>"
},
"geometry":{
"type":"MultiLineString",
"coordinates":[
[
[
-79.7066775992172,
43.6462189758028
],
[
-79.7066939830514,
43.6461985074393
],
[
-79.7066378408013,
43.6461605607267
],
[
-79.7066097743239,
43.6461403201406
],
[
-79.7064548987452,
43.6460566901385
],
[
-79.7063956692058,
43.6460219372408
]
],
[
[
-79.7063956692058,
43.6460219372408
],
[
-79.7063852230813,
43.646033518772
],
[
-79.7063166536656,
43.6461172032157
],
[
-79.7064079964431,
43.6461815121163
],
[
-79.7060589374119,
43.646518038823
],
[
-79.7060054211382,
43.6465600820263
]
],
[
[
-79.7060054211382,
43.6465600820263
],
[
-79.7052588394648,
43.6471387374653
],
[
-79.7048261689477,
43.6474817773536
],
[
-79.7043239742464,
43.6474733374216
],
[
-79.7041128202014,
43.6476608859429
],
[
-79.703901284037,
43.6478509811517
],
[
-79.7030237720306,
43.6486568653637
],
[
-79.7029267563095,
43.6486965678914
]
],
[
[
-79.7029267563091,
43.6486965678909
],
[
-79.7028702942784,
43.6487267896104
],
[
-79.7028019515802,
43.6487884267869
]
],
[
[
-79.6949803205847,
43.6554816862022
],
[
-79.6946328513629,
43.6552226727517
],
[
-79.6945439505269,
43.6551559442016
]
],
[
[
-79.6945439505269,
43.6551559442016
],
[
-79.694066895687,
43.654797865403
],
[
-79.6934193769725,
43.6543136334174
],
[
-79.6924271403494,
43.6535711706703
],
[
-79.6920107752268,
43.6532605761111
],
[
-79.6919943721596,
43.6532604342567
]
],
[
[
-79.6919943721591,
43.6532604342562
],
[
-79.6914713751595,
43.6536247980162
],
[
-79.6911279733848,
43.6533992300817
]
],
[
[
-79.6959960003114,
43.6400049378117
],
[
-79.6960571265341,
43.6400850012767
],
[
-79.6961629127738,
43.640012603549
],
[
-79.6962380127401,
43.6399612066507
],
[
-79.6964991971409,
43.6401581219518
],
[
-79.6965504313169,
43.6403222661559
]
],
[
[
-79.6965504313164,
43.6403222661554
],
[
-79.6963411179014,
43.6405181683405
]
],
[
[
-79.6973635087052,
43.6393434514529
],
[
-79.6975152035274,
43.6394534198075
],
[
-79.6974394413309,
43.6393984974797
],
[
-79.6977214702725,
43.6396029481089
]
],
[
[
-79.7037279098659,
43.6441816734685
],
[
-79.7038116627627,
43.6442425378655
],
[
-79.7043663390943,
43.6446488071586
],
[
-79.7048680167224,
43.645024186195
],
[
-79.7053904212546,
43.6454260322038
],
[
-79.7059251921243,
43.6458354437457
],
[
-79.7065612964782,
43.6461582999466
],
[
-79.7065835802603,
43.6461492141531
],
[
-79.7066775992172,
43.6462189758028
]
],
[
[
-79.6973112420145,
43.6393143090171
],
[
-79.6972862146857,
43.6393319663604
],
[
-79.6971872210635,
43.6392588377729
],
[
-79.6968127129063,
43.6395272639245
],
[
-79.6966669835105,
43.6395263250713
],
[
-79.6960609039152,
43.6399594999986
]
],
[
[
-79.6960609039152,
43.6399594999986
],
[
-79.6960181044663,
43.639990047741
]
],
[
[
-79.7032573776668,
43.6438035217788
],
[
-79.7032773647046,
43.6438182749637
],
[
-79.7032968727787,
43.6438287137889
]
],
[
[
-79.6982280590368,
43.6399996458065
],
[
-79.6983123840689,
43.6400655275078
],
[
-79.6988209765837,
43.6404574862051
],
[
-79.6997681798983,
43.6411574341786
],
[
-79.699722618622,
43.6411857721075
],
[
-79.7003863588722,
43.641677233397
],
[
-79.7008842734269,
43.6420484764154
],
[
-79.7014070294285,
43.6424381587765
],
[
-79.7024395226368,
43.643210754341
],
[
-79.7029823771679,
43.643602976107
],
[
-79.7032573776668,
43.6438035217788
]
]
]
}
}
]
}
This is the first time I am using GeoJSON, so I will need assistance with the GeoJSON format needed for to add the properties and styles. Plus the javascript and Google Map API code.
If anyone can provide some code or a link to some resources which give detailed examples, that would be greatly appreciated.

One option would be to process the GeoJSON as it is loaded, creating google.maps.Polyline objects from each section of the line. Use function closure (like is used for markers in the answer to this question, set the position of the infowindow to the first point of the polyline segment.
proof of concept fiddle
code snippet:
var colors = ["#FF0000", "#800000", "#00FF00", "#008000", "#0000FF", " #8A2BE2", "#A52A2A", "#DEB887", "#5F9EA0", "#000080", "#FFFF00", "#808000", "#FF00FF", "#800080", "#00FFFF", "#7FFFD4", "#008080", "#000000"];
var infowindow = new google.maps.InfoWindow();
function initialize() {
// Create a simple map.
features = [];
map = new google.maps.Map(document.getElementById('map-canvas'), {
zoom: 14,
center: {
lat: 43.65,
lng: -79.7
}
});
// process the loaded GeoJSON data.
var bounds = new google.maps.LatLngBounds();
google.maps.event.addListener(map.data, 'addfeature', function(e) {
if (e.feature.getGeometry().getType() === 'MultiLineString') {
var polys = e.feature.getGeometry().getArray();
for (var i = 0; i < polys.length; i++) {
for (var j = 0; j < polys[i].getLength(); j++) {
var poly = new google.maps.Polyline({
map: map,
path: polys[i].getArray(),
strokeColor: colors[i % colors.length]
});
google.maps.event.addListener(poly, 'click', (function(poly, i, feature) {
return function() {
infowindow.setContent("polyline " + i+"<br>"+feature.getProperty("Line"+i));
infowindow.setPosition(polys[i].getAt(0));
infowindow.open(map);
}
})(poly, i, e.feature));
bounds.extend(polys[i].getAt(j));
}
}
map.fitBounds(bounds);
map.data.setMap(null);
} else if (e.feature.getGeometry().getType() === 'GeometryCollection') {
var polys = e.feature.getGeometry().getArray();
for (var i = 0; i < polys.length; i++) {
for (var j = 0; j < polys[i].getLength(); j++) {
bounds.extend(polys[i].getAt(j));
}
}
map.fitBounds(bounds);
}
});
map.data.addGeoJson(data);
}
google.maps.event.addDomListener(window, 'load', initialize);
var data = {
"type": "FeatureCollection",
"features": [{
"type": "Feature",
"properties": {
"Key": "007",
"Line1": "<this is line 1 desc>",
"Line2": "<this is line 2 desc>",
"Line3": "<this is line 3 desc>",
"Line4": "<this is line 4 desc>",
"Line5": "<this is line 5 desc>",
"Line6": "<this is line 6 desc>",
"Line7": "<this is line 7 desc>"
},
"geometry": {
"type": "MultiLineString",
"coordinates": [
[
[-79.7066775992172,
43.6462189758028
],
[-79.7066939830514,
43.6461985074393
],
[-79.7066378408013,
43.6461605607267
],
[-79.7066097743239,
43.6461403201406
],
[-79.7064548987452,
43.6460566901385
],
[-79.7063956692058,
43.6460219372408
]
],
[
[-79.7063956692058,
43.6460219372408
],
[-79.7063852230813,
43.646033518772
],
[-79.7063166536656,
43.6461172032157
],
[-79.7064079964431,
43.6461815121163
],
[-79.7060589374119,
43.646518038823
],
[-79.7060054211382,
43.6465600820263
]
],
[
[-79.7060054211382,
43.6465600820263
],
[-79.7052588394648,
43.6471387374653
],
[-79.7048261689477,
43.6474817773536
],
[-79.7043239742464,
43.6474733374216
],
[-79.7041128202014,
43.6476608859429
],
[-79.703901284037,
43.6478509811517
],
[-79.7030237720306,
43.6486568653637
],
[-79.7029267563095,
43.6486965678914
]
],
[
[-79.7029267563091,
43.6486965678909
],
[-79.7028702942784,
43.6487267896104
],
[-79.7028019515802,
43.6487884267869
]
],
[
[-79.6949803205847,
43.6554816862022
],
[-79.6946328513629,
43.6552226727517
],
[-79.6945439505269,
43.6551559442016
]
],
[
[-79.6945439505269,
43.6551559442016
],
[-79.694066895687,
43.654797865403
],
[-79.6934193769725,
43.6543136334174
],
[-79.6924271403494,
43.6535711706703
],
[-79.6920107752268,
43.6532605761111
],
[-79.6919943721596,
43.6532604342567
]
],
[
[-79.6919943721591,
43.6532604342562
],
[-79.6914713751595,
43.6536247980162
],
[-79.6911279733848,
43.6533992300817
]
],
[
[-79.6959960003114,
43.6400049378117
],
[-79.6960571265341,
43.6400850012767
],
[-79.6961629127738,
43.640012603549
],
[-79.6962380127401,
43.6399612066507
],
[-79.6964991971409,
43.6401581219518
],
[-79.6965504313169,
43.6403222661559
]
],
[
[-79.6965504313164,
43.6403222661554
],
[-79.6963411179014,
43.6405181683405
]
],
[
[-79.6973635087052,
43.6393434514529
],
[-79.6975152035274,
43.6394534198075
],
[-79.6974394413309,
43.6393984974797
],
[-79.6977214702725,
43.6396029481089
]
],
[
[-79.7037279098659,
43.6441816734685
],
[-79.7038116627627,
43.6442425378655
],
[-79.7043663390943,
43.6446488071586
],
[-79.7048680167224,
43.645024186195
],
[-79.7053904212546,
43.6454260322038
],
[-79.7059251921243,
43.6458354437457
],
[-79.7065612964782,
43.6461582999466
],
[-79.7065835802603,
43.6461492141531
],
[-79.7066775992172,
43.6462189758028
]
],
[
[-79.6973112420145,
43.6393143090171
],
[-79.6972862146857,
43.6393319663604
],
[-79.6971872210635,
43.6392588377729
],
[-79.6968127129063,
43.6395272639245
],
[-79.6966669835105,
43.6395263250713
],
[-79.6960609039152,
43.6399594999986
]
],
[
[-79.6960609039152,
43.6399594999986
],
[-79.6960181044663,
43.639990047741
]
],
[
[-79.7032573776668,
43.6438035217788
],
[-79.7032773647046,
43.6438182749637
],
[-79.7032968727787,
43.6438287137889
]
],
[
[-79.6982280590368,
43.6399996458065
],
[-79.6983123840689,
43.6400655275078
],
[-79.6988209765837,
43.6404574862051
],
[-79.6997681798983,
43.6411574341786
],
[-79.699722618622,
43.6411857721075
],
[-79.7003863588722,
43.641677233397
],
[-79.7008842734269,
43.6420484764154
],
[-79.7014070294285,
43.6424381587765
],
[-79.7024395226368,
43.643210754341
],
[-79.7029823771679,
43.643602976107
],
[-79.7032573776668,
43.6438035217788
]
]
]
}
}]
}
html,
body,
#map-canvas {
height: 100%;
margin: 0px;
padding: 0px;
width: 100%;
}
<script src="https://maps.googleapis.com/maps/api/js"></script>
<div id="map-canvas"></div>

Determine what geoJSON polygon a point is in

I have two data frames, one that contains lat / long points and another that contains geojson data to draw multiple polygons.
The first dataframe (countyDF) is imported from a CSV and the 2nd dataframe (basinData) is imported from geoJSON using readLines() (should I be using getJSON() instead for the json data?
i.e. (Over simplified lat and long, let me know if a more realistic example would help)
countyDF
pointNum Lat Long
1 100 251
2 150 175
3 50 -330
4 -150 100
and geoJSON formatted like this(basinData):
{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "Basin_ID": "9-19", "Basin_Subb": "9-19", "Basin_Name": "TIA JUANA", "Subbasin_N": null }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -117.0679768595155, 32.574291366336219 ], [ -117.06716593683379, 32.573400317874729 ], [ -117.06341997467541, 32.569091397019029 ], [ -117.06194315333148, 32.566356289257612 ], [ -117.0590009947062, 32.565022113434004 ], [ -117.05426277324793, 32.561991889800737 ], [ -117.04796901017907, 32.559156827796407 ], [ -117.03949416454982, 32.555722526353854 ], [ -117.03658670964059, 32.55260623337746 ], [ -117.03547505795123, 32.551186682532702 ], [ -117.03106791328587, 32.547633284977714 ], [ -117.02453341519558, 32.542766715943806 ], [ -117.03096733611001, 32.542203418941845 ], [ -117.0324558562139, 32.542073586201752 ], [ -117.03444148063983, 32.541897693349327 ], [ -117.03508530679723, 32.541840746762325 ], [ -117.03893675722108, 32.541503138385245 ], [ -117.04563848478635, 32.540916090254136 ], [ -117.04690666072744, 32.540805072734159 ], [ -117.04975321345368, 32.540555662782488 ], [ -117.05487638996065, 32.540105955834157 ], [ -117.05619151994331, 32.539990441256123 ], [ -117.05848531771566, 32.539788923486533 ], [ -117.0631749250457, 32.54215137136817 ], [ -117.06680187749691, 32.543877383980934 ], [ -117.06927539222501, 32.545562859775892 ], [ -117.07152258989198, 32.546046045900979 ], [ -117.07489884859852, 32.54530496670057 ], [ -117.07773834685484, 32.54485919550919 ], [ -117.0794889459513, 32.544200153274701 ], [ -117.08117134135811, 32.543542769155188 ], [ -117.08203266401881, 32.542495002174064 ], [ -117.08180018990477, 32.541005184972832 ], [ -117.07878527133475, 32.53800290854668 ], [ -117.08430738067622, 32.537515913009408 ], [ -117.08445861115598, 32.541710736294391 ], [ -117.08701141119104, 32.543969332906386 ], [ -117.08926545491882, 32.544796327561301 ], [ -117.09253292944108, 32.545435592907872 ], [ -117.09470680391962, 32.545631754871643 ], [ 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{ "type": "Feature", "properties": { "Basin_ID": "9-18", "Basin_Subb": "9-18", "Basin_Name": "OTAY VALLEY", "Subbasin_N": null }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -117.09094993890868, 32.619563585315568 ], [ -117.09024988989258, 32.616130450534968 ], [ -117.08912052342582, 32.606956836308925 ], [ -117.08936013555311, 32.601840287378998 ], [ -117.08875364427725, 32.598575478052986 ], [ -117.08680278644947, 32.595962898606246 ], [ -117.08441502669503, 32.595310423365085 ], [ -117.08156822333883, 32.595469231360795 ], [ -117.07566674287644, 32.595559902113109 ], [ -117.06972154230749, 32.596914588847568 ], [ -117.06432122834885, 32.598375496484095 ], [ -117.05964154380946, 32.598503627441907 ], [ -117.05616095566411, 32.597465378923445 ], [ -117.05457753753359, 32.596282762027229 ], [ -117.05073717232032, 32.594157791168556 ], [ -117.04589484061222, 32.592852213251177 ], [ -117.04312260910274, 32.593353146236865 ], [ -117.03899112955028, 32.593759832413419 ], [ 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]
}
I want to test point numbers 1,2,3,4 against all "Basin_ID"'s and if they are within that basin, add that as a column to countyDF.
For example if point 1 were in basin 9-18 and none of the other points fell within polygons contained in basinData the returned data frame would look like the following...
returnedDF:
pointNum Lat Long Basin
1 100 251 9-18
2 150 175 n/a
3 50 -330 n/a
4 -150 100 n/a
Might anybody suggest a specific library / method / solution of accomplishing this? I imagine if there's a library that tests if a point is in a polygod I can loop over the 2nd dataframe for each point?

Here is one way you could do it, using rgdal to read the geojson (see this answer for more details on this) and sp and/or rgeos to test if a point lies within a polygon or not.
Note, I adjusted your coordinates, since none of them was located within a polygon.
First, read the data:
countyDF <- read.table(textConnection("
pointNum Lat Long
1 32.6 -117.1
2 90 175
4 -90 100"), header = TRUE)
basinDF <- rgdal::readOGR("basin.json", "OGRGeoJSON")
Make sure points and polygons have the same projection:
sp::coordinates(countyDF) <- ~Long+Lat
sp::proj4string(countyDF) <- sp::proj4string(basinDF)
Here we use sp::over to extract the attributes of basinDF at each point. If points are not located within a polygon of basinDF NA is returned.
sp::over(countyDF, basinDF)
# Basin_ID Basin_Subb Basin_Name Subbasin_N
# 1 9-18 9-18 OTAY VALLEY <NA>
# 2 <NA> <NA> <NA> <NA>
# 3 <NA> <NA> <NA> <NA>
Alternatively, you could also use rgeos, which tells you that point 1 is located in poygon 1.
rgeos::gWithin(countyDF, basinDF, byid = TRUE)
# 1 2 3
# 0 FALSE FALSE FALSE
# 1 TRUE FALSE FALSE

Default values for a method

I'm trying to specify default values to a Map. Is this how it's done?
static def AddOrder( String key, Map order = [
id: '',
campaign_id: '',
email_id: '',
email: '',
total: 0.0d,
order_date: '',
shipping: 0.0d,
tax: 0.0d,
store_id: '',
store_name: '',
items: [
line_num: 0,
product_id: 0,
sku: '',
product_name: '',
category_id: 0,
qty: 0.0d,
cost: 0.0d
]
] ){
contactMC( key, action, order)
}

Why don't you simply test it? A very short program will show you that indeed, that's how default map parameters work:
def testMethod(Map map = [ foo: 'bar' ]) {
return map.foo
}
println testMethod() //outputs bar
println testMethod([foo:'baz']) //outputs baz

Validation before bulkinsert in sql server

I want to import data from text file.which contain arround lakhs of records
I am using bulk insert do it like this
BULK
INSERT vw_bulk_insert_test
FROM '\\server\c$\csvtext.txt'--\\server\SQLEXPRESS\csvtest.txt'
WITH
(FIRSTROW=2,
check_CONSTRAINTS,
FIELDTERMINATOR = '~',
ROWTERMINATOR = '\n'
)
GO
But before insert I want to validate values of each column without using cursor.Like if second row will have values of all fields except unit_number(Column) then it should create a error log specifying unit_number value is missing.

Personally, I would bulk-insert into a temp table, and then do validations/conversions from the temp table into the table where things will ultimately reside using either TSQL or TSQL in the form of stored procedures created for this purpose.

You have this syntax
BULK INSERT
[ database_name . [ schema_name ] . | schema_name . ] [ table_name | view_name ]
FROM 'data_file'
[ WITH
(
[ [ , ] BATCHSIZE = batch_size ]
[ [ , ] CHECK_CONSTRAINTS ]
[ [ , ] CODEPAGE = { 'ACP' | 'OEM' | 'RAW' | 'code_page' } ]
[ [ , ] DATAFILETYPE =
{ 'char' | 'native'| 'widechar' | 'widenative' } ]
[ [ , ] FIELDTERMINATOR = 'field_terminator' ]
[ [ , ] FIRSTROW = first_row ]
[ [ , ] FIRE_TRIGGERS ]
[ [ , ] FORMATFILE = 'format_file_path' ]
[ [ , ] KEEPIDENTITY ]
[ [ , ] KEEPNULLS ]
[ [ , ] KILOBYTES_PER_BATCH = kilobytes_per_batch ]
[ [ , ] LASTROW = last_row ]
[ [ , ] MAXERRORS = max_errors ]
[ [ , ] ORDER ( { column [ ASC | DESC ] } [ ,...n ] ) ]
[ [ , ] ROWS_PER_BATCH = rows_per_batch ]
[ [ , ] ROWTERMINATOR = 'row_terminator' ]
[ [ , ] TABLOCK ]
[ [ , ] ERRORFILE = 'file_name' ]
)]

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