ContentOutputStream cos = new ContentOutputStream(10, Curl.tmpDir);
        cos.write(new byte[] { 0, 1, 2 });
        assertTrue(cos.isInMemory());
        cos.write(new byte[] { 3, 4 });
        assertTrue(cos.isInMemory());
        cos.write(new byte[] { 5 });
        assertTrue(cos.isInMemory());
        assertFalse(cos.done);
        cos.close();
    }

    @Test

    }

    /**
     * Returns whether the content is cached in memory.
     *
     * @return true if the content is in memory, false if it is in a file
     */
    public boolean isInMemory() {
        return data != null;
    }

    /**
     * Returns the content as a byte array.
     * If the content is cached in memory, a clone of the data is returned.

        }
    }

    // --- isInMemory() tests ---

    @Test
    public void testIsInMemory_WithByteArray() {
        // ## Arrange ##
        ContentCache cache = new ContentCache("test".getBytes());

        // ## Assert ##
        assertTrue(cache.isInMemory());
    }

    @Test
    public void testIsInMemory_WithFile() throws IOException {

                }
                dfos.flush();
                final ContentCache contentCache;
                logger.fine(() -> "Response in " + (dfos.isInMemory() ? "Memory" : "File"));
                if (dfos.isInMemory()) {
                    contentCache = new ContentCache(dfos.getData());
                } else {
                    contentCache = new ContentCache(dfos.getFile());
                }

        // requireNonNull is safe because we always have either `file` or `memory`.
        requireNonNull(memory);
        return new ByteArrayInputStream(memory.getBuffer(), 0, memory.getCount());
      }
    }

    synchronized void reset() throws IOException {
      try {
        close();
      } finally {
        if (memory == null) {
          memory = new MemoryOutput();
        } else {
          memory.reset();

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

          "build-cache",
          "flow-services",
          "input-tracking",
          "internal-integ-testing",
          "model-core",
          "persistent-cache",
          "process-memory-services",
          "scala",
          "snapshots",
          "testing-base-infrastructure"
        ],
        "parallelizationMethod": {
          "name": "TestDistribution"
        }
      },

* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

## Memory { #memory }

If you run **a single process per container** you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

* Startup'ta çalıştırma
* Restart'lar
* Replication (çalışan process sayısı)
* Memory
* Başlatmadan önceki adımlar

## Memory { #memory }

**Container başına tek process** çalıştırırsanız, her container'ın tüketeceği memory miktarı aşağı yukarı tanımlı, stabil ve sınırlı olur (replication varsa birden fazla container için).

You could also use it to stream **large binary files**, where you stream each chunk of data as you read it, without having to read it all in memory at once.

You could also stream **video** or **audio** this way, it could even be generated as you process and send it.

## A `StreamingResponse` with `yield` { #a-streamingresponse-with-yield }