Stop gam­bling with your data in­tegri­ty.

From de­bunk­ing the "XA is slow" myth to solv­ing cloud-na­tive re­cov­ery with LogCloud, we are show­ing you how to re­claim your de­vel­op­er pro­duc­tiv­i­ty and bring 100% atom­ic­i­ty to your mi­croser­vices.

Let's face it: most "re­silient" mi­croser­vice de­signs are still los­ing data.

It is not that de­vel­op­ers aren't try­ing. They are. You see it in the end­less retry log­ic, idem­po­ten­cy to­kens, and cir­cuit break­ers piled into every ser­vice. You see it in all the apolo­getic blog posts ex­plain­ing why 'even­tu­al con­sis­ten­cy' is a fea­ture, not a bug.

But here is the un­com­fort­able truth: these pat­terns are treat­ing the symp­toms, not the dis­ease. If you've ever faced du­pli­cate mes­sages, in­con­sis­tent state across ser­vices, or the dread­ed par­tial fail­ure in a dis­trib­uted sys­tem, then you have seen it up close.

The root cause? Miss­ing trans­ac­tion­al­i­ty.

In the world of mi­croser­vices and light-weight in­fra­struc­ture, a lot of teams think they should roll their own even­tu­al con­sis­ten­cy "pat­terns" - let's call this "cus­tom even­tu­al con­sis­ten­cy". So they end up cod­ing a lot that needs to be test­ed for bor­der-case sce­nar­ios. Even if this is done, the re­sult rarely cov­ers all cas­es, and not every­body wants to main­tain this kind of code.

If you are build­ing mi­croser­vices, you have prob­a­bly heard of the Saga pat­tern. Maybe you are even us­ing it. After all, it is the de­fault an­swer you will find on Stack Over­flow and in most con­fer­ence talks when peo­ple ask: How do I man­age dis­trib­uted trans­ac­tions?

Syn­chro­nous mi­croser­vices look great on pa­per.

They are easy to read, easy to de­bug, and they map per­fect­ly to how we think: “I send a re­quest, I get a re­ply.” No sur­pris­es, right?

Ex­cept when things go wrong. Which in dis­trib­uted sys­tems is most of the time.