# Storage Synchronization

## How Deep Lake Datasets are Synchronized with Long-Term Storage

{% hint style="danger" %}
**Using `with` context when updating Deep Lake datasets is critical for achieving rapid write performance.**
{% endhint %}

### BAD PRACTICE - Code without `with` context

By default, any standalone update to a Deep Lake dataset is immediately pushed to the dataset's long-term storage location. Due to the sheer number of discreet write operations, there may be a significant increase in runtime, especially when the data is stored in the cloud. In the example below, an update is pushed to storage for every call to the `.append()` command.

```python
for i in range(10):
    ds.my_tensor.append(i)
```

### Code using `with` context

To reduce the runtime when using Deep Lake, the `with` syntax below significantly improves performance because it only pushes updates to long-term storage after the code block inside the `with` statement has been executed, or when the local cache is full. This significantly reduces the number of discreet write operations, thereby increasing the speed by up to 100X. 

```python
with ds:
    for i in range(10):
        ds.my_tensor.append(i)
```


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