Syncing and Packing

A guide to syncing and packing

Syncing and Packing

One of the first things you'll do when mining is sync and pack some or all of the weave data.

Syncing

"Syncing" refers to the process of downloading data from network peers. When you launch your miner you'll configure a set of storage modules that cover some or all of the weave. Your node will continuously check for any gaps in your configured data and then search out peers from which to download the missing data.

Packing

Storage modules can be either "unpacked" (e.g. storage_module 16,unpacked) or "packed" (e.g. storage_module 16,En2eqsVJARnTVOSh723PBXAKGmKgrGSjQ2YIGwE_ZRI). Before you can mine data it must be packed to your mining address. There are two symmetric operations that fall under the "packing" umbrella:

1. pack - Symmetrically encrypt a chunk of data with your mining address.

2. unpack - Decrypt a packed chunk of data.

Both operations consume roughly the same amount of computational power. See the benchmarking guide for more details.

Note: You will almost always have to unpack data when syncing it. Whichever peer you sync the data from will likely return it to you packed to its own address. Before you can do anything with it you will first need to unpack. You may then have to pack it to your own address. i.e. each chunk of data synced will usually need 1-2 packing operations.

Storage Module Format

Each storage module has 2 directories chunk_storage and rocksdb.

chunk_storage

This directory stores the actual weave data in a series of roughly 2GB sparse files. Each file contains 8000 chunks stored as << Offset, ChunkData >>.

• Offset is a 3-byte integer used to handle partial chunks (i.e. chunks less than 256KiB). This is only relevant for unpacked data as packed chunks are always 256 KiB.

• ChunkData is the 256 KiB (262,144 bytes) chunk data.

The data may be packed or unpacked (both formats take up the same amount of space). The maximum file size is 2,097,176,000 bytes. Each file is named with the starting offset of the chunks it contains (e.g. chunk_storage file 75702992896000 stores the 8000 chunks starting at weave offset 75,702,992,896,000).

A full partition will contain 3.6 TB (3,600,000,000,000 bytes) of chunk data. Depending on how you've configured your storage modules, your chunk_storage directory may only store a subset of a partition.

For reasons explained below you will rarely be able to sync a full 3.6TB partition. However, your node will continue to search the network for missing chunks so while unlikely it is possible that a previously "dormant" chunk_storage directory to see some activity if previously missing chunk data comes online. In general, though, once you have "fully" synced a storage_module you would expect there to be no further writes to the chunk_storage directory. Below we provide an estimate of each partition's "full synced" size.

rocksdb

The rocksdb directory contains several RocksDB databases used to store metadata related to chunk data (e.g. record keeping, indexing, proofs, etc..).

The exact size of the rocksdb directory will vary over time - unlike chunk_storage you should expect the rocksdb directory to continue to be written to as long as your node is running. The current rough size of a rocksdb directory is ~100 GB (although it will vary from partition to partition and node to node).

Partition Sizes

You will find as you sync data that you're never able to download a full 3.6TB partition - and in fact some partitions seem to stop syncing well short of the 3.6TB. There are 2 steps when adding data to the Arweave network:

1. Submit a transaction with a hash of the data and a fee to reserve space in the weave.

2. Upload the data to the weave (aka seeding).

The data that is missing from a "fully synced" partition is either data that has been filtered out by your content policies (or the content policies of your peers), or it is data that was never seeded by the uploader.

Typically there are 2 reasons why a user might not seed data after they've paid for it:

1. They're just testing / exploring the protocol

2. They're a miner experimenting with sacrifice mining

Sacrifice Mining

Sacrifice Mining is a mining strategy where a miner will pay to upload data to the weave but then never actually seed it. Instead they will keep and mine the data themselves, only sharing the chunks required for any blocks they mine. The premise is that all of this "sacrificed data" gives them a hashrate boost that other miners don't have (since the other miners are unable to mine the unseeded data).

Sam has a good thread describing this.

The important bits:

1. Both in practice and based on economic models: sacrifice mining is not profitable. The cost to reserve space on the weave exceeds the incremental revenue a miner can hope to get from the additional hashrate. The payback period for the initial investment is long and gets longer as the weave grows - in practice it is likely that a miner is never able to recoupe their initial investment.

2. Putting aside the profitability of sacrifice mining, it is ultimately good for the network as a whole. Sam breaks down why this is in his thread.

That said if you look through the partition size data below you'll notice 2 periods where partition sizes are materially smaller than the expected 3.6TB (partitions 0-8, and 30-32). We believe these correspond to periods when miners were experimenting with the strategy, ultimately abandoning it as they realized it was unprofitable.

Latest Estimated Partition Sizes

See table here

Note: These numbers are mostly reliable, but there is always a chance that a previously "fully synced" partition grows in size (though never greater than 3.6TB). This can happen any time the original uploader decides to finally seed their previously unseeded data. In practice this gets less and less likely the older a partition is.

Performance Tips

There are 3 primary bottlenecks when syncing and packing:

1. Your network bandwidth (used to download chunks from peers)

2. Your CPU (used to pack and unpack chunks)

3. Your disk write speed (used to write chunks to disk)

And to a lesser degree:

4. RAM (more heavily used in mining than in syncing/packing, but can become a bottleneck under certain situations)

If any of the 3 primary resources are maxed out: congratulations! Your configuration is syncing and packing as fast as it can!

Increasing Bandwidth

Not much to do here other than negotiate a faster internet connection, or find a second one.

Increasing CPU

Packing and unpacking can be parallelized across chunks, so you can add more cores or increase the clock speed to increase your packing speed. See the hardware guide for guidance on evaluating CPU pack speed.

Increasing Disk Write Speed

During the syncing and packing phase you will typically hit a network bandwidth or CPU bottleneck before you hit a disk IO bottleneck (the reverse is true once you start mining).

If you believe you've hit a disk IO bottleneck you have a few options.

First, confirm that you're getting your expected disk write speed. You can use tools like fio or dd to measure your disks write speed. If it is below your expected speed, you'll want to check your system configuration (software and hardware) for issues.

Second, you can add more disks to your node. This is really only relevant if you have partitions that you intend to sync and pack but which you haven't added to your node configuration. As a general rule you should add all your storage modules to your node while syncing and packing as this will increase your disk IO bandwidth as well as help fully use your network and CPU bandwidth.

Third, you can buy faster disks. This is generally not recommended to unblock a syncing and packing bottleneck as there's a good chance that extra IO speed will go unused once you start mining. Including it here for completeness.

Increasing RAM

The RAM guidelines mentioned in the Mining Guide are focused on mining. Often RAM is not a primary bottleneck during syncing and packing. If you are maxing out your RAM: review the guidelines below. It's possible you can optimize your node configuration.

Increasing Utilization

Okay, so you've reviewed your bottlenecks and determined that none of them are at capacity. Here are some tips to increase syncing and packing speed.

sync_jobs

The sync_jobs flag controls the number of concurrent requests your node will make to peers to download data. The default is 100. Increasing this number should increase your utilization of all resources: the amount of data you pull from peers (network bandwidth), the number of chunks you're packing (CPU), and the volume of data written to disk (disk IO).

However, it is possible to increase this number too much. This can:

1. Cause your node to be rate-limited / throttled by peers and ultimately decrease your bandwidth utilization.

2. Increase your RAM utilization due to backed up sync jobs. This is particularly common if your miner has a poor network connection (e.g. high latency or data loss). Increasing the volume of slow/hanging requests can cause a backup which eventually leads to an out of memory error.

Setting sync_jobs to 200 or even 400 is unlikely to cause any issues. But before you increase it even further our recommendation is to first confirm:

1. Your CPU and network bandwidths are below capacity

2. Your network connectivity is good (e.g. using tools like mtr to track packet loss)

packing_rate

In general you shouldn't need to change packing_rate - the default value is usually more than high enough. That said you can increase it with minimal risk of negative consequences. Consult our benchmarking guide to determine your CPU's maximum packing rate and set packing_rate to something slightly higher.

storage_module

As mentioned above under Increasing Disk Write Speed syncing to all your storage modules at once will maximize your available disk write bandwidth. The same applied to network bandwidth. Adding more storage modules when syncing increases the set of peers you can pull data from (as different peers share different parts of the weave data). This will help your node maximize its network bandwidth by pulling from the "best" set of peers available at a given time.

Repacking

If you configure your node to repack from one local storage module to another the node will prioritize that over pulling data from peers. This can cause you to max out your CPU capacity while your network bandwidth stays low.

This is not a problem. It simply means your node will max out its CPU doing local repacks before it begins searching for peers to download more data from. If you'd rather focus on syncing, just make sure to configure your node without any repacking. Two examples of configurations that will cause local repacking:

1. storage_module 9,unpacked storage_module 9,En2eqsVJARnTVOSh723PBXAKGmKgrGSjQ2YIGwE_ZRI

2. storage_module 16,Q5EfKawrRazp11HEDf_NJpxjYMV385j21nlQNjR8_pY storage_module 16,En2eqsVJARnTVOSh723PBXAKGmKgrGSjQ2YIGwE_ZRI

Note: as mentioned earlier, whenever you sync data - even if you are syncing to unpacked - you will likely have to perform at least one packing operation.

Multiple Full Replicas

If you intend to build more than 1 packed full replica, the following approach should get you there fastest:

1. Download all the data to unpacked storage modules

2. Build each packed replica by locally repacking from your unpacked storage modules

3. You can either keep the unpacked data around for later, or, you can do a repack_in_place when building your final packed replica.

This approach will reduce your download time (since you only have to download the data once) and reduce the number of packing operations (since you only have to unpack peer data once).

Note: This approach is not recommended if your goal is to have 1 or fewer packed replicas. It will work, but won't be any faster than just syncing straight to packed storage modules.