I am looking to add a simple search field, would like to use something like
collectionRef.where('name', 'contains', 'searchTerm')
I tried using where('name', '==', '%searchTerm%'), but it didn't return anything.
I agree with #Kuba's answer, But still, it needs to add a small change to work perfectly for search by prefix. here what worked for me
For searching records starting with name queryText
collectionRef
.where('name', '>=', queryText)
.where('name', '<=', queryText+ '\uf8ff')
The character \uf8ff used in the query is a very high code point in the Unicode range (it is a Private Usage Area [PUA] code). Because it is after most regular characters in Unicode, the query matches all values that start with queryText.
Full-Text Search, Relevant Search, and Trigram Search!
UPDATE - 2/17/21 - I created several new Full Text Search Options.
See Code.Build for details.
Also, side note, dgraph now has websockets for realtime... wow, never saw that coming, what a treat! Cloud Dgraph - Amazing!
--Original Post--
A few notes here:
1.) \uf8ff works the same way as ~
2.) You can use a where clause or start end clauses:
ref.orderBy('title').startAt(term).endAt(term + '~');
is exactly the same as
ref.where('title', '>=', term).where('title', '<=', term + '~');
3.) No, it does not work if you reverse startAt() and endAt() in every combination, however, you can achieve the same result by creating a second search field that is reversed, and combining the results.
Example: First you have to save a reversed version of the field when the field is created. Something like this:
// collection
const postRef = db.collection('posts')
async function searchTitle(term) {
// reverse term
const termR = term.split("").reverse().join("");
// define queries
const titles = postRef.orderBy('title').startAt(term).endAt(term + '~').get();
const titlesR = postRef.orderBy('titleRev').startAt(termR).endAt(termR + '~').get();
// get queries
const [titleSnap, titlesRSnap] = await Promise.all([
titles,
titlesR
]);
return (titleSnap.docs).concat(titlesRSnap.docs);
}
With this, you can search the last letters of a string field and the first, just not random middle letters or groups of letters. This is closer to the desired result. However, this won't really help us when we want random middle letters or words. Also, remember to save everything lowercase, or a lowercase copy for searching, so case won't be an issue.
4.) If you have only a few words, Ken Tan's Method will do everything you want, or at least after you modify it slightly. However, with only a paragraph of text, you will exponentially create more than 1MB of data, which is bigger than firestore's document size limit (I know, I tested it).
5.) If you could combine array-contains (or some form of arrays) with the \uf8ff trick, you might could have a viable search that does not reach the limits. I tried every combination, even with maps, and a no go. Anyone figures this out, post it here.
6.) If you must get away from ALGOLIA and ELASTIC SEARCH, and I don't blame you at all, you could always use mySQL, postSQL, or neo4Js on Google Cloud. They are all 3 easy to set up, and they have free tiers. You would have one cloud function to save the data onCreate() and another onCall() function to search the data. Simple...ish. Why not just switch to mySQL then? The real-time data of course! When someone writes DGraph with websocks for real-time data, count me in!
Algolia and ElasticSearch were built to be search-only dbs, so there is nothing as quick... but you pay for it. Google, why do you lead us away from Google, and don't you follow MongoDB noSQL and allow searches?
There's no such operator, allowed ones are ==, <, <=, >, >=.
You can filter by prefixes only, for example for everything that starts between bar and foo you can use
collectionRef
.where('name', '>=', 'bar')
.where('name', '<=', 'foo')
You can use external service like Algolia or ElasticSearch for that.
While Kuba's answer is true as far as restrictions go, you can partially emulate this with a set-like structure:
{
'terms': {
'reebok': true,
'mens': true,
'tennis': true,
'racket': true
}
}
Now you can query with
collectionRef.where('terms.tennis', '==', true)
This works because Firestore will automatically create an index for every field. Unfortunately this doesn't work directly for compound queries because Firestore doesn't automatically create composite indexes.
You can still work around this by storing combinations of words but this gets ugly fast.
You're still probably better off with an outboard full text search.
While Firebase does not explicitly support searching for a term within a string,
Firebase does (now) support the following which will solve for your case and many others:
As of August 2018 they support array-contains query. See: https://firebase.googleblog.com/2018/08/better-arrays-in-cloud-firestore.html
You can now set all of your key terms into an array as a field then query for all documents that have an array that contains 'X'. You can use logical AND to make further comparisons for additional queries. (This is because firebase does not currently natively support compound queries for multiple array-contains queries so 'AND' sorting queries will have to be done on client end)
Using arrays in this style will allow them to be optimized for concurrent writes which is nice! Haven't tested that it supports batch requests (docs don't say) but I'd wager it does since its an official solution.
Usage:
collection("collectionPath").
where("searchTermsArray", "array-contains", "term").get()
Per the Firestore docs, Cloud Firestore doesn't support native indexing or search for text fields in documents. Additionally, downloading an entire collection to search for fields client-side isn't practical.
Third-party search solutions like Algolia and Elastic Search are recommended.
I'm sure Firebase will come out with "string-contains" soon to capture any index[i] startAt in the string...
But
I’ve researched the webs and found this solution thought of by someone else
set up your data like this
state = { title: "Knitting" };
// ...
const c = this.state.title.toLowerCase();
var array = [];
for (let i = 1; i < c.length + 1; i++) {
array.push(c.substring(0, i));
}
firebase
.firestore()
.collection("clubs")
.doc(documentId)
.update({
title: this.state.title,
titleAsArray: array
});
query like this
firebase.firestore()
.collection("clubs")
.where(
"titleAsArray",
"array-contains",
this.state.userQuery.toLowerCase()
)
As of today (18-Aug-2020), there are basically 3 different workarounds, which were suggested by the experts, as answers to the question.
I have tried them all. I thought it might be useful to document my experience with each one of them.
Method-A: Using: (dbField ">=" searchString) & (dbField "<=" searchString + "\uf8ff")
Suggested by #Kuba & #Ankit Prajapati
.where("dbField1", ">=", searchString)
.where("dbField1", "<=", searchString + "\uf8ff");
A.1 Firestore queries can only perform range filters (>, <, >=, <=) on a single field. Queries with range filters on multiple fields are not supported. By using this method, you can't have a range operator in any other field on the db, e.g. a date field.
A.2. This method does NOT work for searching in multiple fields at the same time. For example, you can't check if a search string is in any of the fileds (name, notes & address).
Method-B: Using a MAP of search strings with "true" for each entry in the map, & using the "==" operator in the queries
Suggested by #Gil Gilbert
document1 = {
'searchKeywordsMap': {
'Jam': true,
'Butter': true,
'Muhamed': true,
'Green District': true,
'Muhamed, Green District': true,
}
}
.where(`searchKeywordsMap.${searchString}`, "==", true);
B.1 Obviously, this method requires extra processing every time data is saved to the db, and more importantly, requires extra space to store the map of search strings.
B.2 If a Firestore query has a single condition like the one above, no index needs to be created beforehand. This solution would work just fine in this case.
B.3 However, if the query has another condition, e.g. (status === "active",) it seems that an index is required for each "search string" the user enters. In other words, if a user searches for "Jam" and another user searches for "Butter", an index should be created beforehand for the string "Jam", and another one for "Butter", etc. Unless you can predict all possible users' search strings, this does NOT work - in case of the query has other conditions!
.where(searchKeywordsMap["Jam"], "==", true); // requires an index on searchKeywordsMap["Jam"]
.where("status", "==", "active");
**Method-C: Using an ARRAY of search strings, & the "array-contains" operator
Suggested by #Albert Renshaw & demonstrated by #Nick Carducci
document1 = {
'searchKeywordsArray': [
'Jam',
'Butter',
'Muhamed',
'Green District',
'Muhamed, Green District',
]
}
.where("searchKeywordsArray", "array-contains", searchString);
C.1 Similar to Method-B, this method requires extra processing every time data is saved to the db, and more importantly, requires extra space to store the array of search strings.
C.2 Firestore queries can include at most one "array-contains" or "array-contains-any" clause in a compound query.
General Limitations:
None of these solutions seems to support searching for partial strings. For example, if a db field contains "1 Peter St, Green District", you can't search for the string "strict."
It is almost impossible to cover all possible combinations of expected search strings. For example, if a db field contains "1 Mohamed St, Green District", you may NOT be able to search for the string "Green Mohamed", which is a string having the words in a different order than the order used in the DB field.
There is no one solution that fits all. Each workaround has its limitations. I hope the information above can help you during the selection process between these workarounds.
For a list of Firestore query conditions, please check out the documentation https://firebase.google.com/docs/firestore/query-data/queries.
I have not tried https://fireblog.io/blog/post/firestore-full-text-search, which is suggested by #Jonathan.
Late answer but for anyone who's still looking for an answer, Let's say we have a collection of users and in each document of the collection we have a "username" field, so if want to find a document where the username starts with "al" we can do something like
FirebaseFirestore.getInstance()
.collection("users")
.whereGreaterThanOrEqualTo("username", "al")
I used trigram just like Jonathan said it.
trigrams are groups of 3 letters stored in a database to help with searching. so if I have data of users and I let' say I want to query 'trum' for donald trump I have to store it this way
and I just to recall this way
onPressed: () {
//LET SAY YOU TYPE FOR 'tru' for trump
List<String> search = ['tru', 'rum'];
Future<QuerySnapshot> inst = FirebaseFirestore.instance
.collection("users")
.where('trigram', arrayContainsAny: search)
.get();
print('result=');
inst.then((value) {
for (var i in value.docs) {
print(i.data()['name']);
}
});
that will get correct result no matter what
EDIT 05/2021:
Google Firebase now has an extension to implement Search with Algolia. Algolia is a full text search platform that has an extensive list of features. You are required to have a "Blaze" plan on Firebase and there are fees associated with Algolia queries, but this would be my recommended approach for production applications. If you prefer a free basic search, see my original answer below.
https://firebase.google.com/products/extensions/firestore-algolia-search
https://www.algolia.com
ORIGINAL ANSWER:
The selected answer only works for exact searches and is not natural user search behavior (searching for "apple" in "Joe ate an apple today" would not work).
I think Dan Fein's answer above should be ranked higher. If the String data you're searching through is short, you can save all substrings of the string in an array in your Document and then search through the array with Firebase's array_contains query. Firebase Documents are limited to 1 MiB (1,048,576 bytes) (Firebase Quotas and Limits) , which is about 1 million characters saved in a document (I think 1 character ~= 1 byte). Storing the substrings is fine as long as your document isn't close to 1 million mark.
Example to search user names:
Step 1: Add the following String extension to your project. This lets you easily break up a string into substrings. (I found this here).
extension String {
var length: Int {
return count
}
subscript (i: Int) -> String {
return self[i ..< i + 1]
}
func substring(fromIndex: Int) -> String {
return self[min(fromIndex, length) ..< length]
}
func substring(toIndex: Int) -> String {
return self[0 ..< max(0, toIndex)]
}
subscript (r: Range<Int>) -> String {
let range = Range(uncheckedBounds: (lower: max(0, min(length, r.lowerBound)),
upper: min(length, max(0, r.upperBound))))
let start = index(startIndex, offsetBy: range.lowerBound)
let end = index(start, offsetBy: range.upperBound - range.lowerBound)
return String(self[start ..< end])
}
Step 2: When you store a user's name, also store the result of this function as an array in the same Document. This creates all variations of the original text and stores them in an array. For example, the text input "Apple" would creates the following array: ["a", "p", "p", "l", "e", "ap", "pp", "pl", "le", "app", "ppl", "ple", "appl", "pple", "apple"], which should encompass all search criteria a user might enter. You can leave maximumStringSize as nil if you want all results, however, if there is long text, I would recommend capping it before the document size gets too big - somewhere around 15 works fine for me (most people don't search long phrases anyway).
func createSubstringArray(forText text: String, maximumStringSize: Int?) -> [String] {
var substringArray = [String]()
var characterCounter = 1
let textLowercased = text.lowercased()
let characterCount = text.count
for _ in 0...characterCount {
for x in 0...characterCount {
let lastCharacter = x + characterCounter
if lastCharacter <= characterCount {
let substring = textLowercased[x..<lastCharacter]
substringArray.append(substring)
}
}
characterCounter += 1
if let max = maximumStringSize, characterCounter > max {
break
}
}
print(substringArray)
return substringArray
}
Step 3: You can use Firebase's array_contains function!
[yourDatabasePath].whereField([savedSubstringArray], arrayContains: searchText).getDocuments....
I just had this problem and came up with a pretty simple solution.
String search = "ca";
Firestore.instance.collection("categories").orderBy("name").where("name",isGreaterThanOrEqualTo: search).where("name",isLessThanOrEqualTo: search+"z")
The isGreaterThanOrEqualTo lets us filter out the beginning of our search and by adding a "z" to the end of the isLessThanOrEqualTo we cap our search to not roll over to the next documents.
I actually think the best solution to do this within Firestore is to put all substrings in an array, and just do an array_contains query. This allows you to do substring matching. A bit overkill to store all substrings but if your search terms are short it's very very reasonable.
If you don't want to use a third-party service like Algolia, Firebase Cloud Functions are a great alternative. You can create a function that can receive an input parameter, process through the records server-side and then return the ones that match your criteria.
This worked for me perfectly but might cause performance issues.
Do this when querying firestore:
Future<QuerySnapshot> searchResults = collectionRef
.where('property', isGreaterThanOrEqualTo: searchQuery.toUpperCase())
.getDocuments();
Do this in your FutureBuilder:
return FutureBuilder(
future: searchResults,
builder: (context, snapshot) {
List<Model> searchResults = [];
snapshot.data.documents.forEach((doc) {
Model model = Model.fromDocumet(doc);
if (searchQuery.isNotEmpty &&
!model.property.toLowerCase().contains(searchQuery.toLowerCase())) {
return;
}
searchResults.add(model);
})
};
Following code snippet takes input from user and acquires data starting with the typed one.
Sample Data:
Under Firebase Collection 'Users'
user1: {name: 'Ali', age: 28},
user2: {name: 'Khan', age: 30},
user3: {name: 'Hassan', age: 26},
user4: {name: 'Adil', age: 32}
TextInput: A
Result:
{name: 'Ali', age: 28},
{name: 'Adil', age: 32}
let timer;
// method called onChangeText from TextInput
const textInputSearch = (text) => {
const inputStart = text.trim();
let lastLetterCode = inputStart.charCodeAt(inputStart.length-1);
lastLetterCode++;
const newLastLetter = String.fromCharCode(lastLetterCode);
const inputEnd = inputStart.slice(0,inputStart.length-1) + lastLetterCode;
clearTimeout(timer);
timer = setTimeout(() => {
firestore().collection('Users')
.where('name', '>=', inputStart)
.where('name', '<', inputEnd)
.limit(10)
.get()
.then(querySnapshot => {
const users = [];
querySnapshot.forEach(doc => {
users.push(doc.data());
})
setUsers(users); // Setting Respective State
});
}, 1000);
};
2021 Update
Took a few things from other answers. This one includes:
Multi word search using split (acts as OR)
Multi key search using flat
A bit limited on case-sensitivity, you can solve this by storing duplicate properties in uppercase. Ex: query.toUpperCase() user.last_name_upper
// query: searchable terms as string
let users = await searchResults("Bob Dylan", 'users');
async function searchResults(query = null, collection = 'users', keys = ['last_name', 'first_name', 'email']) {
let querySnapshot = { docs : [] };
try {
if (query) {
let search = async (query)=> {
let queryWords = query.trim().split(' ');
return queryWords.map((queryWord) => keys.map(async (key) =>
await firebase
.firestore()
.collection(collection)
.where(key, '>=', queryWord)
.where(key, '<=', queryWord + '\uf8ff')
.get())).flat();
}
let results = await search(query);
await (await Promise.all(results)).forEach((search) => {
querySnapshot.docs = querySnapshot.docs.concat(search.docs);
});
} else {
// No query
querySnapshot = await firebase
.firestore()
.collection(collection)
// Pagination (optional)
// .orderBy(sortField, sortOrder)
// .startAfter(startAfter)
// .limit(perPage)
.get();
}
} catch(err) {
console.log(err)
}
// Appends id and creates clean Array
const items = [];
querySnapshot.docs.forEach(doc => {
let item = doc.data();
item.id = doc.id;
items.push(item);
});
// Filters duplicates
return items.filter((v, i, a) => a.findIndex(t => (t.id === v.id)) === i);
}
Note: the number of Firebase calls is equivalent to the number of words in the query string * the number of keys you're searching on.
Same as #nicksarno but with a more polished code that doesn't need any extension:
Step 1
func getSubstrings(from string: String, maximumSubstringLenght: Int = .max) -> [Substring] {
let string = string.lowercased()
let stringLength = string.count
let stringStartIndex = string.startIndex
var substrings: [Substring] = []
for lowBound in 0..<stringLength {
for upBound in lowBound..<min(stringLength, lowBound+maximumSubstringLenght) {
let lowIndex = string.index(stringStartIndex, offsetBy: lowBound)
let upIndex = string.index(stringStartIndex, offsetBy: upBound)
substrings.append(string[lowIndex...upIndex])
}
}
return substrings
}
Step 2
let name = "Lorenzo"
ref.setData(["name": name, "nameSubstrings": getSubstrings(from: name)])
Step 3
Firestore.firestore().collection("Users")
.whereField("nameSubstrings", arrayContains: searchText)
.getDocuments...
With Firestore you can implement a full text search but it will still cost more reads than it would have otherwise, and also you'll need to enter and index the data in a particular way, So in this approach you can use firebase cloud functions to tokenise and then hash your input text while choosing a linear hash function h(x) that satisfies the following - if x < y < z then h(x) < h (y) < h(z). For tokenisation you can choose some lightweight NLP Libraries in order to keep the cold start time of your function low that can strip unnecessary words from your sentence. Then you can run a query with less than and greater than operator in Firestore.
While storing your data also, you'll have to make sure that you hash the text before storing it, and store the plain text also as if you change the plain text the hashed value will also change.
Typesense service provide substring search for Firebase Cloud Firestore database.
https://typesense.org/docs/guide/firebase-full-text-search.html
Following is the relevant codes of typesense integration for my project.
lib/utils/typesense.dart
import 'dart:convert';
import 'package:flutter_instagram_clone/model/PostModel.dart';
import 'package:http/http.dart' as http;
class Typesense {
static String baseUrl = 'http://typesense_server_ip:port/';
static String apiKey = 'xxxxxxxx'; // your Typesense API key
static String resource = 'collections/postData/documents/search';
static Future<List<PostModel>> search(String searchKey, int page, {int contentType=-1}) async {
if (searchKey.isEmpty) return [];
List<PostModel> _results = [];
var header = {'X-TYPESENSE-API-KEY': apiKey};
String strSearchKey4Url = searchKey.replaceFirst('#', '%23').replaceAll(' ', '%20');
String url = baseUrl +
resource +
'?q=${strSearchKey4Url}&query_by=postText&page=$page&sort_by=millisecondsTimestamp:desc&num_typos=0';
if(contentType==0)
{
url += "&filter_by=isSelling:false";
} else if(contentType == 1)
{
url += "&filter_by=isSelling:true";
}
var response = await http.get(Uri.parse(url), headers: header);
var data = json.decode(response.body);
for (var item in data['hits']) {
PostModel _post = PostModel.fromTypeSenseJson(item['document']);
if (searchKey.contains('#')) {
if (_post.postText.toLowerCase().contains(searchKey.toLowerCase()))
_results.add(_post);
} else {
_results.add(_post);
}
}
print(_results.length);
return _results;
}
static Future<List<PostModel>> getHubPosts(String searchKey, int page,
{List<String>? authors, bool? isSelling}) async {
List<PostModel> _results = [];
var header = {'X-TYPESENSE-API-KEY': apiKey};
String filter = "";
if (authors != null || isSelling != null) {
filter += "&filter_by=";
if (isSelling != null) {
filter += "isSelling:$isSelling";
if (authors != null && authors.isNotEmpty) {
filter += "&&";
}
}
if (authors != null && authors.isNotEmpty) {
filter += "authorID:$authors";
}
}
String url = baseUrl +
resource +
'?q=${searchKey.replaceFirst('#', '%23')}&query_by=postText&page=$page&sort_by=millisecondsTimestamp:desc&num_typos=0$filter';
var response = await http.get(Uri.parse(url), headers: header);
var data = json.decode(response.body);
for (var item in data['hits']) {
PostModel _post = PostModel.fromTypeSenseJson(item['document']);
_results.add(_post);
}
print(_results.length);
return _results;
}
}
lib/services/hubDetailsService.dart
import 'package:flutter/material.dart';
import 'package:flutter_instagram_clone/model/PostModel.dart';
import 'package:flutter_instagram_clone/utils/typesense.dart';
class HubDetailsService with ChangeNotifier {
String searchKey = '';
List<String>? authors;
bool? isSelling;
int nContentType=-1;
bool isLoading = false;
List<PostModel> hubResults = [];
int _page = 1;
bool isMore = true;
bool noResult = false;
Future initSearch() async {
isLoading = true;
isMore = true;
noResult = false;
hubResults = [];
_page = 1;
List<PostModel> _results = await Typesense.search(searchKey, _page, contentType: nContentType);
for(var item in _results) {
hubResults.add(item);
}
isLoading = false;
if(_results.length < 10) isMore = false;
if(_results.isEmpty) noResult = true;
notifyListeners();
}
Future nextPage() async {
if(!isMore) return;
_page++;
List<PostModel> _results = await Typesense.search(searchKey, _page);
hubResults.addAll(_results);
if(_results.isEmpty) {
isMore = false;
}
notifyListeners();
}
Future refreshPage() async {
isLoading = true;
notifyListeners();
await initSearch();
isLoading = false;
notifyListeners();
}
Future search(String _searchKey) async {
isLoading = true;
notifyListeners();
searchKey = _searchKey;
await initSearch();
isLoading = false;
notifyListeners();
}
}
lib/ui/hub/hubDetailsScreen.dart
import 'package:flutter/cupertino.dart';
import 'package:flutter/material.dart';
import 'package:flutter_instagram_clone/constants.dart';
import 'package:flutter_instagram_clone/main.dart';
import 'package:flutter_instagram_clone/model/MessageData.dart';
import 'package:flutter_instagram_clone/model/SocialReactionModel.dart';
import 'package:flutter_instagram_clone/model/User.dart';
import 'package:flutter_instagram_clone/model/hubModel.dart';
import 'package:flutter_instagram_clone/services/FirebaseHelper.dart';
import 'package:flutter_instagram_clone/services/HubService.dart';
import 'package:flutter_instagram_clone/services/helper.dart';
import 'package:flutter_instagram_clone/services/hubDetailsService.dart';
import 'package:flutter_instagram_clone/ui/fullScreenImageViewer/FullScreenImageViewer.dart';
import 'package:flutter_instagram_clone/ui/home/HomeScreen.dart';
import 'package:flutter_instagram_clone/ui/hub/editHubScreen.dart';
import 'package:provider/provider.dart';
import 'package:smooth_page_indicator/smooth_page_indicator.dart';
class HubDetailsScreen extends StatefulWidget {
final HubModel hub;
HubDetailsScreen(this.hub);
#override
_HubDetailsScreenState createState() => _HubDetailsScreenState();
}
class _HubDetailsScreenState extends State<HubDetailsScreen> {
late HubDetailsService _service;
List<SocialReactionModel?> _reactionsList = [];
final fireStoreUtils = FireStoreUtils();
late Future<List<SocialReactionModel>> _myReactions;
final scrollController = ScrollController();
bool _isSubLoading = false;
#override
void initState() {
// TODO: implement initState
super.initState();
_service = Provider.of<HubDetailsService>(context, listen: false);
print(_service.isLoading);
init();
}
init() async {
_service.searchKey = "";
if(widget.hub.contentWords.length>0)
{
for(var item in widget.hub.contentWords) {
_service.searchKey += item + " ";
}
}
switch(widget.hub.contentType) {
case 'All':
break;
case 'Marketplace':
_service.isSelling = true;
_service.nContentType = 1;
break;
case 'Post Only':
_service.isSelling = false;
_service.nContentType = 0;
break;
case 'Keywords':
break;
}
for(var item in widget.hub.exceptWords) {
if(item == 'Marketplace') {
_service.isSelling = _service.isSelling != null?true:false;
} else {
_service.searchKey += "-" + item + "";
}
}
if(widget.hub.fromUserType == 'Followers') {
List<User> _followers = await fireStoreUtils.getFollowers(MyAppState.currentUser!.userID);
_service.authors = [];
for(var item in _followers)
_service.authors!.add(item.userID);
}
if(widget.hub.fromUserType == 'Selected') {
_service.authors = widget.hub.fromUserIds;
}
_service.initSearch();
_myReactions = fireStoreUtils.getMyReactions()
..then((value) {
_reactionsList.addAll(value);
});
scrollController.addListener(pagination);
}
void pagination(){
if(scrollController.position.pixels ==
scrollController.position.maxScrollExtent) {
_service.nextPage();
}
}
#override
Widget build(BuildContext context) {
Provider.of<HubDetailsService>(context);
PageController _controller = PageController(
initialPage: 0,
);
return Scaffold(
backgroundColor: Colors.white,
body: RefreshIndicator(
onRefresh: () async {
_service.refreshPage();
},
child: CustomScrollView(
controller: scrollController,
slivers: [
SliverAppBar(
centerTitle: false,
expandedHeight: MediaQuery.of(context).size.height * 0.25,
pinned: true,
backgroundColor: Colors.white,
title: Row(
mainAxisAlignment: MainAxisAlignment.spaceBetween,
children: [
InkWell(
onTap: (){
Navigator.pop(context);
},
child: Container(
width: 35, height: 35,
decoration: BoxDecoration(
color: Colors.white,
borderRadius: BorderRadius.circular(20)
),
child: Center(
child: Icon(Icons.arrow_back),
),
),
),
if(widget.hub.user.userID == MyAppState.currentUser!.userID)
InkWell(
onTap: () async {
var _hub = await push(context, EditHubScreen(widget.hub));
if(_hub != null) {
Navigator.pop(context, true);
}
},
child: Container(
width: 35, height: 35,
decoration: BoxDecoration(
color: Colors.white,
borderRadius: BorderRadius.circular(20)
),
child: Center(
child: Icon(Icons.edit, color: Colors.black, size: 20,),
),
),
),
],
),
automaticallyImplyLeading: false,
flexibleSpace: FlexibleSpaceBar(
collapseMode: CollapseMode.pin,
background: Container(color: Colors.grey,
child: Stack(
children: [
PageView.builder(
controller: _controller,
itemCount: widget.hub.medias.length,
itemBuilder: (context, index) {
Url postMedia = widget.hub.medias[index];
return GestureDetector(
onTap: () => push(
context,
FullScreenImageViewer(
imageUrl: postMedia.url)),
child: displayPostImage(postMedia.url));
}),
if (widget.hub.medias.length > 1)
Padding(
padding: const EdgeInsets.only(bottom: 30.0),
child: Align(
alignment: Alignment.bottomCenter,
child: SmoothPageIndicator(
controller: _controller,
count: widget.hub.medias.length,
effect: ScrollingDotsEffect(
dotWidth: 6,
dotHeight: 6,
dotColor: isDarkMode(context)
? Colors.white54
: Colors.black54,
activeDotColor: Color(COLOR_PRIMARY)),
),
),
),
],
),
)
),
),
_service.isLoading?
SliverFillRemaining(
child: Center(
child: CircularProgressIndicator(),
),
):
SliverList(
delegate: SliverChildListDelegate([
if(widget.hub.userId != MyAppState.currentUser!.userID)
_isSubLoading?
Center(
child: Padding(
padding: EdgeInsets.all(5),
child: CircularProgressIndicator(),
),
):
Padding(
padding: EdgeInsets.symmetric(horizontal: 5),
child: widget.hub.shareUserIds.contains(MyAppState.currentUser!.userID)?
ElevatedButton(
onPressed: () async {
setState(() {
_isSubLoading = true;
});
await Provider.of<HubService>(context, listen: false).unsubscribe(widget.hub);
setState(() {
_isSubLoading = false;
widget.hub.shareUserIds.remove(MyAppState.currentUser!.userID);
});
},
style: ElevatedButton.styleFrom(
primary: Colors.red
),
child: Text(
"Unsubscribe",
),
):
ElevatedButton(
onPressed: () async {
setState(() {
_isSubLoading = true;
});
await Provider.of<HubService>(context, listen: false).subscribe(widget.hub);
setState(() {
_isSubLoading = false;
widget.hub.shareUserIds.add(MyAppState.currentUser!.userID);
});
},
style: ElevatedButton.styleFrom(
primary: Colors.green
),
child: Text(
"Subscribe",
),
),
),
Padding(
padding: EdgeInsets.all(15,),
child: Text(
widget.hub.name,
style: TextStyle(
color: Colors.black,
fontSize: 18,
fontWeight: FontWeight.bold
),
),
),
..._service.hubResults.map((e) {
if(e.isAuction && (e.auctionEnded || DateTime.now().isAfter(e.auctionEndTime??DateTime.now()))) {
return Container();
}
return PostWidget(post: e);
}).toList(),
if(_service.noResult)
Padding(
padding: EdgeInsets.all(20),
child: Text(
'No results for this hub',
style: TextStyle(
fontSize: 18,
fontWeight: FontWeight.bold
),
),
),
if(_service.isMore)
Center(
child: Container(
padding: EdgeInsets.all(5),
child: CircularProgressIndicator(),
),
)
]),
)
],
),
)
);
}
}
You can try using 2 lambdas and S3. These resources are very cheap and you will only be charged once the app has extreme usage ( if the business model is good then high usage -> higher income).
The first lambda will be used to push a text-document mapping to an S3 json file.
the second lambda will basically be your search api, you will use it to query the JSON in s3 and return the results.
The drawback will probably be the latency from s3 to lambda.
I use this with Vue js
query(collection(db,'collection'),where("name",">=",'searchTerm'),where("name","<=","~"))
I also couldn't manage to create a search function to Firebase using the suggestions and Firebase tools so I created my own "field-string contains search-string(substring) check", using the .contains() Kotlin function:
firestoreDB.collection("products")
.get().addOnCompleteListener { task->
if (task.isSuccessful){
val document = task.result
if (!document.isEmpty) {
if (document != null) {
for (documents in document) {
var name = documents.getString("name")
var type = documents.getString("type")
if (name != null && type != null) {
if (name.contains(text, ignoreCase = true) || type.contains(text, ignoreCase = true)) {
// do whatever you want with the document
} else {
showNoProductsMsg()
}
}
}
}
binding.progressBarSearch.visibility = View.INVISIBLE
} else {
showNoProductsMsg()
}
} else{
showNoProductsMsg()
}
}
First, you get ALL the documents in the collection you want, then you filter them using:
for (documents in document) {
var name = documents.getString("name")
var type = documents.getString("type")
if (name != null && type != null) {
if (name.contains(text, ignoreCase = true) || type.contains(text, ignoreCase = true)) {
//do whatever you want with this document
} else {
showNoProductsMsg()
}
}
}
In my case, I filtered them all by the name of the product and its type, then I used the boolean name.contains(string, ignoreCase = true) OR type.contains(string, ignoreCase = true, string is the text I got in the search bar of my app and I recommend you to use ignoreCase = true. With this setence being true, you can do whatever you want with the document.
I guess this is the best workaround since Firestore only supports number and exacts strings queries, so if your code didn't work doing this:
collection.whereGreaterThanOrEqualTo("name", querySearch)
collection.whereLessThanOrEqualTo("name", querySearch)
You're welcome :) because what I did works!
Firebase suggests Algolia or ElasticSearch for Full-Text search, but a cheaper alternative might be MongoDB. The cheapest cluster (approx US$10/mth) allows you to index for full-text.
We can use the back-tick to print out the value of a string. This should work:
where('name', '==', `${searchTerm}`)
Is there any framework/library to help writing fixed length flat files in java?
I want to write a collection of beans/entities into a flat file without worrying with convertions, padding, alignment, fillers, etcs
For example, I'd like to parse a bean like:
public class Entity{
String name = "name"; // length = 10; align left; fill with spaces
Integer id = 123; // length = 5; align left; fill with spaces
Integer serial = 321 // length = 5; align to right; fill with '0'
Date register = new Date();// length = 8; convert to yyyyMMdd
}
... into ...
name 123 0032120110505
mikhas 5000 0122120110504
superuser 1 0000120101231
...
You're not likely to encounter a framework that can cope with a "Legacy" system's format. In most cases, Legacy systems don't use standard formats, but frameworks expect them. As a maintainer of legacy COBOL systems and Java/Groovy convert, I encounter this mismatch frequently. "Worrying with conversions, padding, alignment, fillers, etcs" is primarily what you do when dealing with a legacy system. Of course, you can encapsulate some of it away into handy helpers. But most likely, you'll need to get real familiar with java.util.Formatter.
For example, you might use the Decorator pattern to create decorators to do the conversion. Below is a bit of groovy (easily convertible into Java):
class Entity{
String name = "name"; // length = 10; align left; fill with spaces
Integer id = 123; // length = 5; align left; fill with spaces
Integer serial = 321 // length = 5; align to right; fill with '0'
Date register = new Date();// length = 8; convert to yyyyMMdd
}
class EntityLegacyDecorator {
Entity d
EntityLegacyDecorator(Entity d) { this.d = d }
String asRecord() {
return String.format('%-10s%-5d%05d%tY%<tm%<td',
d.name,d.id,d.serial,d.register)
}
}
def e = new Entity(name: 'name', id: 123, serial: 321, register: new Date('2011/05/06'))
assert new EntityLegacyDecorator(e).asRecord() == 'name 123 0032120110506'
This is workable if you don't have too many of these and the objects aren't too complex. But pretty quickly the format string gets intolerable. Then you might want decorators for Date, like:
class DateYMD {
Date d
DateYMD(d) { this.d = d }
String toString() { return d.format('yyyyMMdd') }
}
so you can format with %s:
String asRecord() {
return String.format('%-10s%-5d%05d%s',
d.name,d.id,d.serial,new DateYMD(d.register))
}
But for significant number of bean properties, the string is still too gross, so you want something that understands columns and lengths that looks like the COBOL spec you were handed, so you'll write something like this:
class RecordBuilder {
final StringBuilder record
RecordBuilder(recordSize) {
record = new StringBuilder(recordSize)
record.setLength(recordSize)
}
def setField(pos,length,String s) {
record.replace(pos - 1, pos + length, s.padRight(length))
}
def setField(pos,length,Date d) {
setField(pos,length, new DateYMD(d).toString())
}
def setField(pos,length, Integer i, boolean padded) {
if (padded)
setField(pos,length, String.format("%0" + length + "d",i))
else
setField(pos,length, String.format("%-" + length + "d",i))
}
String toString() { record.toString() }
}
class EntityLegacyDecorator {
Entity d
EntityLegacyDecorator(Entity d) { this.d = d }
String asRecord() {
RecordBuilder record = new RecordBuilder(28)
record.setField(1,10,d.name)
record.setField(11,5,d.id,false)
record.setField(16,5,d.serial,true)
record.setField(21,8,d.register)
return record.toString()
}
}
After you've written enough setField() methods to handle you legacy system, you'll briefly consider posting it on GitHub as a "framework" so the next poor sap doesn't have to to it again. But then you'll consider all the ridiculous ways you've seen COBOL store a "date" (MMDDYY, YYMMDD, YYDDD, YYYYDDD) and numerics (assumed decimal, explicit decimal, sign as trailing separate or sign as leading floating character). Then you'll realize why nobody has produced a good framework for this and occasionally post bits of your production code into SO as an example... ;)
If you are still looking for a framework, check out BeanIO at http://www.beanio.org
uniVocity-parsers goes a long way to support tricky fixed-width formats, including lines with different fields, paddings, etc.
Check out this example to write imaginary client & accounts details. This uses a lookahead value to identify which format to use when writing a row:
FixedWidthFields accountFields = new FixedWidthFields();
accountFields.addField("ID", 10); //account ID has length of 10
accountFields.addField("Bank", 8); //bank name has length of 8
accountFields.addField("AccountNumber", 15); //etc
accountFields.addField("Swift", 12);
//Format for clients' records
FixedWidthFields clientFields = new FixedWidthFields();
clientFields.addField("Lookahead", 5); //clients have their lookahead in a separate column
clientFields.addField("ClientID", 15, FieldAlignment.RIGHT, '0'); //let's pad client ID's with leading zeroes.
clientFields.addField("Name", 20);
FixedWidthWriterSettings settings = new FixedWidthWriterSettings();
settings.getFormat().setLineSeparator("\n");
settings.getFormat().setPadding('_');
//If a record starts with C#, it's a client record, so we associate "C#" with the client format.
settings.addFormatForLookahead("C#", clientFields);
//Rows starting with #A should be written using the account format
settings.addFormatForLookahead("A#", accountFields);
StringWriter out = new StringWriter();
//Let's write
FixedWidthWriter writer = new FixedWidthWriter(out, settings);
writer.writeRow(new Object[]{"C#",23234, "Miss Foo"});
writer.writeRow(new Object[]{"A#23234", "HSBC", "123433-000", "HSBCAUS"});
writer.writeRow(new Object[]{"A#234", "HSBC", "222343-130", "HSBCCAD"});
writer.writeRow(new Object[]{"C#",322, "Mr Bar"});
writer.writeRow(new Object[]{"A#1234", "CITI", "213343-130", "CITICAD"});
writer.close();
System.out.println(out.toString());
The output will be:
C#___000000000023234Miss Foo____________
A#23234___HSBC____123433-000_____HSBCAUS_____
A#234_____HSBC____222343-130_____HSBCCAD_____
C#___000000000000322Mr Bar______________
A#1234____CITI____213343-130_____CITICAD_____
This is just a rough example. There are many other options available, including support for annotated java beans, which you can find here.
Disclosure: I'm the author of this library, it's open-source and free (Apache 2.0 License)
The library Fixedformat4j is a pretty neat tool to do exactly this: http://fixedformat4j.ancientprogramming.com/
Spring Batch has a FlatFileItemWriter, but that won't help you unless you use the whole Spring Batch API.
But apart from that, I'd say you just need a library that makes writing to files easy (unless you want to write the whole IO code yourself).
Two that come to mind are:
Guava
Files.write(stringData, file, Charsets.UTF_8);
Commons / IO
FileUtils.writeStringToFile(file, stringData, "UTF-8");
Don't know of any frame work but you can just use RandomAccessFile. You can position the file pointer to anywhere in the file to do your reads and writes.
I've just find a nice library that I'm using:
http://sourceforge.net/apps/trac/ffpojo/wiki
Very simple to configurate with XML or annotations!
A simple way to write beans/entities to a flat file is to use ObjectOutputStream.
public static void writeToFile(File file, Serializable object) throws IOException {
ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(file));
oos.writeObject(object);
oos.close();
}
You can write to a fixed length flat file with
FileUtils.writeByteArrayToFile(new File(filename), new byte[length]);
You need to be more specific about what you want to do with the file. ;)
Try FFPOJO API as it has everything which you need to create a flat file with fixed lengths and also it will convert a file to an object and vice versa.
#PositionalRecord
public class CFTimeStamp {
String timeStamp;
public CFTimeStamp(String timeStamp) {
this.timeStamp = timeStamp;
}
#PositionalField(initialPosition = 1, finalPosition = 26, paddingAlign = PaddingAlign.RIGHT, paddingCharacter = '0')
public String getTimeStamp() {
return timeStamp;
}
#Override
public String toString() {
try {
FFPojoHelper ffPojo = FFPojoHelper.getInstance();
return ffPojo.parseToText(this);
} catch (FFPojoException ex) {
trsLogger.error(ex.getMessage(), ex);
}
return null;
}
}