Is multiple assignment of tuples slower than multiple assignment statements? - scala

Is there a difference between assigning multiple variables using a tuple, and assigning them in multiple statements?
For example, is there a difference between the following code snippets?
// multiple assignment using tuples
val (x, y) = (str.length, str.substring(1, 2))
// multiple-statement assignment
val x = str.length
val y = str.substring(1, 2)

There is a difference. The approach of using tuples is actually invoking an extractor (the unapply method), which would incur a cost at runtime. The second approach is certainly faster.
To get an idea about the difference, here is a decompilation of two methods showing both approaches. You can see clearly how the first approach causes much more operations.
An important point to note here is that the first expression requires auto-boxing to a java.lang.Integer (because Tuple2 accepts objects), while the second expression uses the value without boxing.
public void m1(java.lang.String);
Code:
0: new #16 // class scala/Tuple2
3: dup
4: aload_1
5: invokevirtual #22 // Method java/lang/String.length:()I
8: invokestatic #28 // Method scala/runtime/BoxesRunTime.boxToInteger:(I)Ljava/lang/Integer;
11: aload_1
12: iconst_1
13: iconst_2
14: invokevirtual #32 // Method java/lang/String.substring:(II)Ljava/lang/String;
17: invokespecial #35 // Method scala/Tuple2."<init>":(Ljava/lang/Object;Ljava/lang/Object;)V
20: astore_3
21: aload_3
22: ifnull 75
25: aload_3
26: invokevirtual #38 // Method scala/Tuple2._1$mcI$sp:()I
29: istore 4
31: aload_3
32: invokevirtual #42 // Method scala/Tuple2._2:()Ljava/lang/Object;
35: checkcast #18 // class java/lang/String
38: astore 5
40: new #16 // class scala/Tuple2
43: dup
44: iload 4
46: invokestatic #28 // Method scala/runtime/BoxesRunTime.boxToInteger:(I)Ljava/lang/Integer;
49: aload 5
51: invokespecial #35 // Method scala/Tuple2."<init>":(Ljava/lang/Object;Ljava/lang/Object;)V
54: astore 6
56: aload 6
58: astore_2
59: aload_2
60: invokevirtual #38 // Method scala/Tuple2._1$mcI$sp:()I
63: istore 7
65: aload_2
66: invokevirtual #42 // Method scala/Tuple2._2:()Ljava/lang/Object;
69: checkcast #18 // class java/lang/String
72: astore 8
74: return
75: new #44 // class scala/MatchError
78: dup
79: aload_3
80: invokespecial #47 // Method scala/MatchError."<init>":(Ljava/lang/Object;)V
83: athrow
public void m2(java.lang.String);
Code:
0: aload_1
1: invokevirtual #22 // Method java/lang/String.length:()I
4: istore_2
5: aload_1
6: iconst_1
7: iconst_2
8: invokevirtual #32 // Method java/lang/String.substring:(II)Ljava/lang/String;
11: astore_3
12: return
}

Related

Mapbox in Android - Black color around GEOTiff raster layers

Goal
I need to add some aeronautical layers taken from FAA to the map. The layers are provided as GeoTIFF files.
Steps
Downloaded a GeoTiff file from FAA website.
Using QGis app clipped the legend from the file. Actually the issue occurs without this step as well.
Reprojected it to EPSG:3857 using GDAL command gdalwarp -q -t_srs EPSG:3857 -dstalpha -of vrt Albuquerque\ SEC\ 104-cut.tif /vsistdout/ | gdal_translate -co compress=lzw /vsistdin/ Albuquerque\ SEC\ 104-north-up-cut.tif. Otherwise I got Error creating Mapnik Datasource: Invalid raster: Invalid rotation value in geotransform array when uploading to Mapbox.
Created a Tileset by uploading GeoTIFFs to Mapbox.
Created a new style in the Mapbox Studio.
Added the tilesets as layers.
Mapbox Studio Result
The map is showing well in Mapbox Studio:
Android Result
However in Android app this style shows with some black borders of random width depending on zoom level and camera position.
Here is how it looks in android:
I tried it on Pixel 3a (Android 10), Nexus 5x (Android 8.1) and Android emulator (Android 10). I have good internet connection and gave it enough time so the tiles are loaded.
The source code where the map is embedded is official Demo app. I just replaced token and style URL:
mapView.getMapAsync(new OnMapReadyCallback() {
#Override
public void onMapReady(#NonNull MapboxMap mapboxMap) {
DefaultStyleActivity.this.mapboxMap = mapboxMap;
mapboxMap.setStyle("mapbox://styles/rustamg/ck8se724l23bh1io2b1hnbqls");
}
});
Here is GDALInfo for the GEOTiff I uploaded:
Driver: GTiff/GeoTIFF
Files: /Users/me/Desktop/Dev/Albuquerque SEC 104-north-up-cut.tif
Size is 16104, 11408
Coordinate System is:
PROJCS["WGS 84 / Pseudo-Mercator",
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.0174532925199433,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]],
PROJECTION["Mercator_1SP"],
PARAMETER["central_meridian",0],
PARAMETER["scale_factor",1],
PARAMETER["false_easting",0],
PARAMETER["false_northing",0],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
AXIS["X",EAST],
AXIS["Y",NORTH],
EXTENSION["PROJ4","+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=#null +wktext +no_defs"],
AUTHORITY["EPSG","3857"]]
Origin = (-12154316.342745549976826,4340308.343459489755332)
Pixel Size = (51.194510520863538,-51.194510520863538)
Metadata:
AREA_OR_POINT=Area
TIFFTAG_DATETIME=2019:09:03 09:02:22
TIFFTAG_RESOLUTIONUNIT=2 (pixels/inch)
TIFFTAG_SOFTWARE=Adobe Photoshop CC (Windows)
TIFFTAG_XRESOLUTION=300
TIFFTAG_YRESOLUTION=300
Image Structure Metadata:
COMPRESSION=LZW
INTERLEAVE=PIXEL
Corner Coordinates:
Upper Left (-12154316.343, 4340308.343) (109d11' 2.69"W, 36d17'16.43"N)
Lower Left (-12154316.343, 3756281.367) (109d11' 2.69"W, 31d56'47.16"N)
Upper Right (-11329879.945, 4340308.343) (101d46'40.96"W, 36d17'16.43"N)
Lower Right (-11329879.945, 3756281.367) (101d46'40.96"W, 31d56'47.16"N)
Center (-11742098.144, 4048294.855) (105d28'51.82"W, 34d 8'42.17"N)
Band 1 Block=16104x1 Type=Byte, ColorInterp=Palette
Mask Flags: PER_DATASET ALPHA
Color Table (RGB with 256 entries)
0: 255,255,255,255
1: 255,255,0,255
2: 255,0,255,255
3: 255,0,0,255
4: 0,255,255,255
5: 0,255,0,255
6: 0,0,255,255
7: 0,0,0,255
8: 252,252,254,255
9: 255,255,1,255
10: 252,236,170,255
11: 248,228,166,255
12: 228,200,154,255
13: 154,81,86,255
14: 120,103,105,255
15: 221,135,154,255
16: 166,87,106,255
17: 107,86,91,255
18: 172,104,124,255
19: 79,9,35,255
20: 141,59,88,255
21: 126,12,61,255
22: 229,107,161,255
23: 200,150,173,255
24: 234,178,204,255
25: 100,8,53,255
26: 200,81,137,255
27: 146,68,105,255
28: 136,38,88,255
29: 98,29,64,255
30: 190,113,151,255
31: 119,44,83,255
32: 214,182,199,255
33: 177,144,163,255
34: 116,13,75,255
35: 33,8,25,255
36: 143,116,141,255
37: 232,216,232,255
38: 248,232,248,255
39: 77,26,86,255
40: 75,69,104,255
41: 105,103,125,255
42: 40,36,105,255
43: 8,8,24,255
44: 232,232,248,255
45: 120,120,122,255
46: 8,14,85,255
47: 203,204,216,255
48: 60,62,80,255
49: 8,22,115,255
50: 86,88,104,255
51: 180,184,201,255
52: 8,54,179,255
53: 8,40,120,255
54: 28,56,120,255
55: 8,40,104,255
56: 143,152,171,255
57: 8,54,136,255
58: 37,47,65,255
59: 184,201,232,255
60: 25,72,152,255
61: 202,214,233,255
62: 31,56,95,255
63: 8,84,182,255
64: 8,72,158,255
65: 8,56,120,255
66: 9,42,86,255
67: 25,72,136,255
68: 37,88,152,255
69: 51,75,104,255
70: 11,100,202,255
71: 8,23,40,255
72: 27,72,120,255
73: 8,56,104,255
74: 44,88,133,255
75: 184,198,212,255
76: 80,84,88,255
77: 8,72,133,255
78: 22,104,178,255
79: 71,121,168,255
80: 91,134,173,255
81: 43,121,184,255
82: 80,113,140,255
83: 8,88,152,255
84: 24,88,136,255
85: 53,104,143,255
86: 8,88,136,255
87: 22,104,149,255
88: 114,144,162,255
89: 139,172,191,255
90: 104,124,134,255
91: 146,205,232,255
92: 136,197,216,255
93: 134,184,201,255
94: 153,200,216,255
95: 135,216,232,255
96: 85,105,109,255
97: 213,233,236,255
98: 152,195,200,255
99: 167,216,221,255
100: 181,232,237,255
101: 151,233,237,255
102: 8,24,24,255
103: 230,248,248,255
104: 182,183,183,255
105: 184,217,216,255
106: 120,136,134,255
107: 24,40,37,255
108: 174,207,200,255
109: 143,147,145,255
110: 225,248,232,255
111: 103,120,104,255
112: 8,24,8,255
113: 87,104,86,255
114: 212,232,210,255
115: 181,200,175,255
116: 163,171,158,255
117: 229,248,216,255
118: 198,216,184,255
119: 147,172,126,255
120: 231,248,200,255
121: 200,216,168,255
122: 216,232,178,255
123: 200,216,151,255
124: 232,248,184,255
125: 212,232,146,255
126: 232,248,167,255
127: 189,207,105,255
128: 199,200,152,255
129: 248,248,71,255
130: 24,24,8,255
131: 145,144,57,255
132: 248,248,104,255
133: 216,216,135,255
134: 248,248,168,255
135: 200,200,136,255
136: 216,216,152,255
137: 232,232,168,255
138: 248,248,184,255
139: 216,216,168,255
140: 232,232,184,255
141: 248,248,200,255
142: 200,200,168,255
143: 120,120,101,255
144: 232,232,200,255
145: 248,248,216,255
146: 216,216,193,255
147: 200,200,184,255
148: 232,232,216,255
149: 248,248,232,255
150: 170,168,56,255
151: 201,194,72,255
152: 179,174,78,255
153: 229,223,101,255
154: 214,205,88,255
155: 196,189,104,255
156: 232,218,133,255
157: 246,233,151,255
158: 42,40,29,255
159: 148,136,82,255
160: 200,184,120,255
161: 216,200,136,255
162: 232,216,152,255
163: 248,232,168,255
164: 165,152,104,255
165: 114,106,76,255
166: 182,173,141,255
167: 248,216,115,255
168: 183,168,119,255
169: 147,139,114,255
170: 200,168,72,255
171: 216,184,88,255
172: 200,184,136,255
173: 216,200,152,255
174: 232,216,168,255
175: 248,232,184,255
176: 228,184,72,255
177: 200,168,88,255
178: 216,184,104,255
179: 81,74,58,255
180: 248,188,71,255
181: 232,185,88,255
182: 248,199,101,255
183: 200,168,104,255
184: 216,184,120,255
185: 232,200,136,255
186: 248,216,152,255
187: 168,152,120,255
188: 200,184,152,255
189: 216,200,168,255
190: 232,216,184,255
191: 219,167,72,255
192: 178,144,82,255
193: 248,231,200,255
194: 184,136,56,255
195: 216,168,88,255
196: 232,185,104,255
197: 248,200,123,255
198: 220,150,49,255
199: 130,93,40,255
200: 200,152,84,255
201: 184,152,104,255
202: 200,168,120,255
203: 216,184,136,255
204: 232,200,152,255
205: 248,216,168,255
206: 179,119,36,255
207: 160,114,54,255
208: 216,168,104,255
209: 232,184,120,255
210: 200,137,56,255
211: 232,168,88,255
212: 61,53,43,255
213: 146,115,80,255
214: 216,168,120,255
215: 248,200,152,255
216: 168,136,104,255
217: 184,152,120,255
218: 200,168,136,255
219: 216,184,152,255
220: 232,200,168,255
221: 248,216,184,255
222: 216,200,184,255
223: 248,232,216,255
224: 110,87,68,255
225: 248,184,135,255
226: 170,151,137,255
227: 184,136,104,255
228: 200,152,120,255
229: 216,168,136,255
230: 232,184,150,255
231: 248,200,168,255
232: 171,114,77,255
233: 168,136,120,255
234: 232,200,184,255
235: 200,183,174,255
236: 232,164,134,255
237: 200,168,154,255
238: 248,167,139,255
239: 171,119,104,255
240: 216,168,154,255
241: 184,133,121,255
242: 140,117,111,255
243: 232,213,208,255
244: 201,148,137,255
245: 235,181,172,255
246: 216,138,126,255
247: 41,24,23,255
248: 24,8,8,255
249: 248,232,232,255
250: 201,200,200,255
251: 248,248,248,255
252: 232,232,232,255
253: 104,104,104,255
254: 24,24,24,255
255: 8,8,8,255
Band 2 Block=16104x1 Type=Byte, ColorInterp=Alpha
Question
How can I get rid of those black issues?
I'd be happy to provide more info if needed.
Update 2020-05-06
I tried what's suggested in troubleshooting page: both prepare tileset and create RasterSource within Android app. The first command rio calc "(asarray (take a 1) (take a 2) (take a 3))" --co compress=lzw --co tiled=true --co blockxsize=256 --co blockysize=256 --name a=filename.tif filename255.tif failed with an error:
IndexError: index 1 is out of bounds for size 1
However my tif already uses LZW compression. So I tried the second command:
rio edit-info --nodata 0 filename255.tif
and uploaded the result to Mapbox.
The result was the same except that white background of the image became black as well:
I also tried the same steps with smaller image (409x306) as it's pointed here that Android has texture size limitation. The result is all the same:
The black borders should be transparent, but the raster image format Mapbox is using (JPG) does not support transparency, which is a known issue that exists in every gl-native-based SDK. This issue presents itself when you reference a style that has the raster layer built in.
As a workaround, you can follow Mapbox's troubleshooting guide on troubleshooting raster images with black backgrounds to make your tileset transparent and then add the tileset as a RasterSource within your Android application to display the raster tile as expected.
Please take a look at the following example as a reference for adding a RasterSource:
Add a WMS Source
Instead of calling the rasterSource using "mapbox://username.tilesetID", you need to call it similarly to the Add a WMS Source example. The code below will resolve your issue:
style.addSource(RasterSource( "albuquerque-source", TileSet( "tileset", "https://api.mapbox.com/v4/<username.tilesetID>/{z}/{x}/{y}.png?access_token=<your_access_token>" ), 256 ))

Huge amount of memory used by flink

Since the last couple week I build a DataStream programs in Flink in scala.
But I have a strange behavior, flink uses lots of more memory than I expected.
I have a 4 ListState of tuple(Int, long) in my processFunction keyed by INT, I use it to get different unique Counter in a different time frame, and I expected the most of the memory was used by this List.
But it's not the case.
So I print an histo live of the JVM.
And I was surprised how many memories are used.
num #instances #bytes class name
----------------------------------------------
1: 138920685 6668192880 java.util.HashMap$Node
2: 138893041 5555721640 org.apache.flink.streaming.api.operators.InternalTimer
3: 149680624 3592334976 java.lang.Integer
4: 48313229 3092046656 org.apache.flink.runtime.state.heap.CopyOnWriteStateTable$StateTableEntry
5: 14042723 2579684280 [Ljava.lang.Object;
6: 4492 2047983264 [Ljava.util.HashMap$Node;
7: 41686732 1333975424 com.myJob.flink.tupleState
8: 201 784339688 [Lorg.apache.flink.runtime.state.heap.CopyOnWriteStateTable$StateTableEntry;
9: 17230300 689212000 com.myJob.flink.uniqStruct
10: 14025040 561001600 java.util.ArrayList
11: 8615581 413547888 com.myJob.flink.Data$FingerprintCnt
12: 6142006 393088384 com.myJob.flink.ProcessCountStruct
13: 4307549 172301960 com.myJob.flink.uniqresult
14: 4307841 137850912 com.myJob.flink.Data$FingerprintUniq
15: 2153904 137849856 com.myJob.flink.Data$StreamData
16: 1984742 79389680 scala.collection.mutable.ListBuffer
17: 1909472 61103104 scala.collection.immutable.$colon$colon
18: 22200 21844392 [B
19: 282624 9043968 org.apache.flink.shaded.netty4.io.netty.buffer.PoolThreadCache$MemoryRegionCache$Entry
20: 59045 6552856 [C
21: 33194 2655520 java.nio.DirectByteBuffer
22: 32804 2361888 sun.misc.Cleaner
23: 35 2294600 [Lscala.concurrent.forkjoin.ForkJoinTask;
24: 640 2276352 [Lorg.apache.flink.shaded.netty4.io.netty.buffer.PoolThreadCache$MemoryRegionCache$Entry;
25: 32768 2097152 org.apache.flink.core.memory.HybridMemorySegment
26: 12291 2082448 java.lang.Class
27: 58591 1874912 java.lang.String
28: 8581 1372960 java.lang.reflect.Method
29: 32790 1311600 java.nio.DirectByteBuffer$Deallocator
30: 18537 889776 java.util.concurrent.ConcurrentHashMap$Node
31: 4239 508680 java.lang.reflect.Field
32: 8810 493360 java.nio.HeapByteBuffer
33: 7389 472896 java.util.HashMap
34: 5208 400336 [I
The tupple(Int, long) is com.myJob.flink.tupleState in 7th position.
And I see the tuple use less than 2G of memory.
I don't understand why flink used this amount of memory for these classes.
Can anyone give me a light on this behavior, thanks in advance.
Update:
I run my job on a stand alone cluster (1 jobManager, 3 taskManager)
the flink version is 1.5-SNAPSHOT commit : e4486ae
I get the histo live on one taskManager node.
Update 2 :
In my processFunction I used :
ctx.timerService.registerProcessingTimeTimer(ctx.timestamp + 100)
And after on onTimer function, I process my listState to check all old data.
so it create a timer for each call on processFunction.
but why the timer is steel on memory after onTimer function triggered
How many windows do you end up with? Based on the top two entries what are are seeing is the "timers" that are used by Flink to track when to clean up the window. For every key in the window you will end up with (key, endTimestamp) effectively in the timer state. If you have a very large number of windows (perhaps out of order time or delayed watermarking) or a very large number of keys in each window, those will each take up memory.
Note that even if you are using RocksDB state, the TimerService uses Heap memory so you have to watch out for that.

Ionic CALL_AND_RETRY_LAST Allocation failed - JavaScript heap out of memory while starting new project

I am new to Ionic I am getting below error when I am creating any new project.
Could any one help me to fix this issue?
==== JS stack trace =========================================
Security context: 0x1240acccf781 <JS Object>
1: build [/usr/local/lib/node_modules/ionic/node_modules/chalk/index.js:118] [pc=0x185922bbdfc] (this=0x1883445b3691 <JS Function Chalk.chalk.template (SharedFunctionInfo 0x1883445b3199)>,_styles=0x36dd597b5061 <JS Array[1]>,key=0x1240acc57f81 <String[4]: bold>)
2: _onTimeout [/usr/local/lib/node_modules/ionic/node_modules/#ionic/cli-utils/lib/utils/task.js:~111] [pc=0x18592337f60] (thi...
FATAL ERROR: CALL_AND_RETRY_LAST Allocation failed - JavaScript heap out of memory
1: node::Abort() [ionic]
2: 0x109f0ac [ionic]
3: v8::Utils::ReportApiFailure(char const*, char const*) [ionic]
4: v8::internal::V8::FatalProcessOutOfMemory(char const*, bool) [ionic]
5: v8::internal::Factory::NewTransitionArray(int) [ionic]
6: v8::internal::TransitionArray::Insert(v8::internal::Handle<v8::internal::Map>, v8::internal::Handle<v8::internal::Name>, v8::internal::Handle<v8::internal::Map>, v8::internal::SimpleTransitionFlag) [ionic]
7: v8::internal::Map::CopyReplaceDescriptors(v8::internal::Handle<v8::internal::Map>, v8::internal::Handle<v8::internal::DescriptorArray>, v8::internal::Handle<v8::internal::LayoutDescriptor>, v8::internal::TransitionFlag, v8::internal::MaybeHandle<v8::internal::Name>, char const*, v8::internal::SimpleTransitionFlag) [ionic]
8: v8::internal::Map::CopyAddDescriptor(v8::internal::Handle<v8::internal::Map>, v8::internal::Descriptor*, v8::internal::TransitionFlag) [ionic]
9: v8::internal::Map::CopyWithField(v8::internal::Handle<v8::internal::Map>, v8::internal::Handle<v8::internal::Name>, v8::internal::Handle<v8::internal::FieldType>, v8::internal::PropertyAttributes, v8::internal::Representation, v8::internal::TransitionFlag) [ionic]
10: v8::internal::Map::TransitionToDataProperty(v8::internal::Handle<v8::internal::Map>, v8::internal::Handle<v8::internal::Name>, v8::internal::Handle<v8::internal::Object>, v8::internal::PropertyAttributes, v8::internal::Object::StoreFromKeyed) [ionic]
11: v8::internal::LookupIterator::PrepareTransitionToDataProperty(v8::internal::Handle<v8::internal::JSObject>, v8::internal::Handle<v8::internal::Object>, v8::internal::PropertyAttributes, v8::internal::Object::StoreFromKeyed) [ionic]
12: v8::internal::StoreIC::LookupForWrite(v8::internal::LookupIterator*, v8::internal::Handle<v8::internal::Object>, v8::internal::Object::StoreFromKeyed) [ionic]
13: v8::internal::StoreIC::UpdateCaches(v8::internal::LookupIterator*, v8::internal::Handle<v8::internal::Object>, v8::internal::Object::StoreFromKeyed) [ionic]
14: v8::internal::StoreIC::Store(v8::internal::Handle<v8::internal::Object>, v8::internal::Handle<v8::internal::Name>, v8::internal::Handle<v8::internal::Object>, v8::internal::Object::StoreFromKeyed) [ionic]
15: v8::internal::Runtime_StoreIC_Miss(int, v8::internal::Object**, v8::internal::Isolate*) [ionic]
16: 0x185921092a7
Aborted (core dumped)
Try with the following command:
$node --max-old-space-size=4096 /usr/local/bin/ionic cordova build android --prod

Unnecessary load and store instruction in scala's byte code

I just did some inverstigation on pattern match and its corresponding byte code.
val a = Array(1,2,3,4)
a.map {
case i => i + 1
}
For above code, I use javap and got the byte code for the annonymous function inside map:
public int apply$mcII$sp(int);
Code:
0: iload_1
1: istore_2
2: iload_2
3: iconst_1
4: iadd
5: ireturn
So it seems to me that in line 0 we push an int (the parameter), then in line 1 we load the int and in line 2 we push it back ... What's the purpose here?
Thanks!
Dude, try -optimise.
public int apply$mcII$sp(int);
flags: ACC_PUBLIC
Code:
stack=2, locals=2, args_size=2
0: iload_1
1: iconst_1
2: iadd
3: ireturn
Use
scala> :javap -prv -
and then something like
scala> :javap -prv $line4/$read$$iw$$iw$$anonfun$1
This is not really an answer, since I couldn't figure out why this happens. I'm hoping that these observations will be at least helpful :)
I'm seeing the following bytecode in Scala 2.10:
public int apply$mcII$sp(int);
Code:
0: iload_1 ; var1 -> stack
1: istore_2 ; var2 <- stack
2: iload_2 ; var2 -> stack
3: iconst_1 ; 1 -> stack
4: iadd
5: istore_3 ; var3 <- stack
6: iload_3 ; var3 -> stack
7: ireturn ; return <- stack
The first two instructions seem to simply move the value of var1 to var2, then move var2 to the stack as a parameter. The same can be observed after iadd, where the result is stored in var3 for no apparent reason, since ireturn returns the value from the stack anyway.

[AVPlaybackItem fpItem]: message sent to deallocated instance

I play a movie with MPMoviePlayerController. Later, the app is "restarted" (meaning a pseudo-reset, where all viewControllers are removed and the user returns to the home screen), and the same movie is played again.
This leads to a crash in iOS 3.2.2 on the iPad:
[AVPlaybackItem fpItem]: message sent
to deallocated instance
I have no idea where that comes from. Seems to be something private. Has anyone experienced and possibly solved the same problem?
The stack trace for that particular address:
(gdb) info malloc 0x11471400
Alloc: Block address: 0x11471400 length: 76
Stack - pthread: 0xa0630500 number of frames: 34
0: 0x9534e0c3 in malloc_zone_calloc
1: 0x9534e01a in calloc
2: 0x343edc9 in _internal_class_createInstanceFromZone
3: 0x344b5c9 in _class_createInstanceFromZone
4: 0x344b5ef in class_createInstance
5: 0x3326b57 in +[NSObject allocWithZone:]
6: 0x332583a in +[NSObject alloc]
7: 0x536ab67 in -[AVPlaybackQueue queueItemWasAddedNotification:]
8: 0x27f586 in _nsnote_callback
9: 0x328d165 in _CFXNotificationPostNotification
10: 0x2762ca in -[NSNotificationCenter postNotificationName:object:userInfo:]
11: 0x5354982 in -[AVQueue itemWasAdded:atIndex:]
12: 0x5354801 in -[AVQueue insertItem:atIndex:error:]
13: 0x53549d8 in -[AVQueue appendItem:error:]
14: 0x535c3be in -[AVController addNextFeederItemToQueue]
15: 0x535b06f in -[AVController checkQueueSpace]
16: 0x5359f46 in -[AVController setQueue:]
17: 0x535ac62 in -[AVController setQueueFeeder:withIndex:]
18: 0x30eee20 in -[MPAVController reloadFeederWithStartIndex:]
19: 0x30deed7 in -[MPMoviePlayerControllerNew _prepareToPlayWithStartIndex:]
20: 0x30dc686 in -[MPMoviePlayerControllerNew prepareToPlay]
21: 0x27f586 in _nsnote_callback
22: 0x328d165 in _CFXNotificationPostNotification
23: 0x2762ca in -[NSNotificationCenter postNotificationName:object:userInfo:]
24: 0x281238 in -[NSNotificationCenter postNotificationName:object:]
25: 0x31596d1 in -[MPMovie _determineMediaType]
26: 0x291b87 in __NSFireDelayedPerform
27: 0x32747dc in CFRunLoopRunSpecific
28: 0x32738a8 in CFRunLoopRunInMode
29: 0x3aaf89d in GSEventRunModal
30: 0x3aaf962 in GSEventRun
31: 0x52b372 in UIApplicationMain
32: 0x27be in main at /blablabla
33: 0x2735 in start
It sounds like you're calling release more than you are calling retain.
Does the error message not contain a hex address at the end? If it does, follow these steps to hunt down the offending object:
Navigate to Project->Edit Active Executable (or press Command-Option-X). Choose the arguments tab. Set the environmental variables as shown below:
Run the program and repeat the steps needed to reproduce the error.
Copy the hex address at the end of the error. Then, in the debugger console type this command: (gdb) info malloc-history <paste-address-here>.
Examine the output to hunt down the offending object.
P.S. Don't forget to disable the environmental variables when you're done.
maybe you call prepare to play more than once for the same movie and i think this is the problem and it exists to all ios prior to 4.3 i guess (not sure though) so just flag the movie if prepare to play was called once then don't recall it for the same file