Ethereum memory leaks in web gut: Python specific analysis

As a Python developer working with Binance Futures API through WebSockets, you are probably not unfamiliar to quickly get a huge amount of data objects. However, when this performance comes at a price – namely memory leaks – it’s time to study the cause and take corrective actions. In this article, we will go into Ethereum’s specific memory leak problem that affects WebSocket connections.

Problem: Reception of Large Data Object

In the outlook, you get about 600 data objects every 250 milliseconds (MS). While this in itself may not seem much, the huge amount of these demands can cause significant performance problems and, finally, memory leaks. As we explore, this is partly due to the ethereum’s specific design shortage on the web coast.

Disadvantages: Frustrated object pools

WebSocket customers object sets are designed to manage a limited number of objects that need to be obtained from the storage (eg database or cache). These pools help optimize performance by re -using the data received. However, Ethereum Web3.You Library – Your Python script has been used for interaction with Ethereum blockchain – it seems to use the implementation of object sets.

Problem: Unoptimized combination of objects

After a more careful examination, it becomes obvious that the mechanism of combining objects Ethereum web3.You are not optimized for low -volume data capture. Specifically, the “ObjectPool” class is on the air.JS(not shown here) is an ineffective object management strategy that causes excessive use of memory when working with large small objects, such as those received from binance for future contracts.

consequences and suggestions

This memory leak can cause significant problems including:

1
Performance degradation : Increased memory consumption can lead to slower performance and reduced reaction in your application.

• Resource Restrictions

  : With the increase in memory use, it can eventually exceed the available system resources, causing an emergency of your application or flawless.

To reduce these effects, consider introducing one of the following strategies:

• Use more efficient object combinations (eg "ObjectPool" custom implementation), which is optimized for small amounts of data.

• Introduce cache and buffering mechanisms To reduce the load on your system and reduce the use of memory at the time of accepting high data.

By solving this problem, you can provide invisible performance and prevent potential emergencies or reduce response to your application.

Here is an updated example of a code fragment that includes a more efficient introduction of objects:

Python

Import asyncio

To import a connection from web links

ASYNC DEF MAIN ():

async with a website.connect ("WSS: //api.binance.com") as web pockets:

Although this is the truth:

Data = Wait for the website.recv ()

Process the data you receive here ...

Use custom object combining implementation (eg, objectpool)

Object_pool = objectpool (1000)

Replace with selected pool size

Try:

Score = Expect object_pool.acquire (Date)

Use the resulting object in the field of action ...

Finally:

If Isinstance (Ob_pool, Objectpool):

Expect object_pool.release (data)

asyncio.get_event_loop ().

Note that this example is simplified and you need to adjust it to your particular use. Also, make sure you use a compatible library to introduce the ObjectPool class.